id	sid	tid	token	lemma	pos
ijassa-121	1	1	advances	advance	NOUN
ijassa-121	1	2	in	in	ADP
ijassa-121	1	3	systems	system	NOUN
ijassa-121	1	4	science	science	NOUN
ijassa-121	1	5	and	and	CCONJ
ijassa-121	1	6	applications	application	NOUN
ijassa-121	1	7	(	(	PUNCT
ijassa-121	1	8	2012	2012	NUM
ijassa-121	1	9	)	)	PUNCT
ijassa-121	1	10	vol.12	vol.12	NOUN
ijassa-121	1	11	no.4	no.4	PROPN
ijassa-121	1	12	388	388	NUM
ijassa-121	1	13	-	-	SYM
ijassa-121	1	14	398	398	NUM
ijassa-121	1	15	optimal	optimal	ADJ
ijassa-121	1	16	designs	design	NOUN
ijassa-121	1	17	in	in	ADP
ijassa-121	1	18	random	random	ADJ
ijassa-121	1	19	intercept	intercept	NOUN
ijassa-121	1	20	model	model	NOUN
ijassa-121	1	21	with	with	ADP
ijassa-121	1	22	heteroscedastic	heteroscedastic	ADJ
ijassa-121	1	23	errors	error	NOUN
ijassa-121	1	24	jing	je	VERB
ijassa-121	1	25	cheng1	cheng1	NOUN
ijassa-121	1	26	and	and	CCONJ
ijassa-121	1	27	rongxian	rongxian	ADJ
ijassa-121	1	28	yue2	yue2	NOUN
ijassa-121	1	29	1department	1department	NUM
ijassa-121	1	30	of	of	ADP
ijassa-121	1	31	mathematics	mathematics	PROPN
ijassa-121	1	32	,	,	PUNCT
ijassa-121	1	33	chaohu	chaohu	PROPN
ijassa-121	1	34	college	college	PROPN
ijassa-121	1	35	,	,	PUNCT
ijassa-121	1	36	anhui	anhui	PROPN
ijassa-121	1	37	238000	238000	NUM
ijassa-121	1	38	,	,	PUNCT
ijassa-121	1	39	china	china	PROPN
ijassa-121	1	40	2department	2department	NUM
ijassa-121	1	41	of	of	ADP
ijassa-121	1	42	mathematics	mathematic	NOUN
ijassa-121	1	43	,	,	PUNCT
ijassa-121	1	44	shanghai	shanghai	PROPN
ijassa-121	1	45	normal	normal	ADJ
ijassa-121	1	46	university	university	PROPN
ijassa-121	1	47	,	,	PUNCT
ijassa-121	1	48	shanghai	shanghai	PROPN
ijassa-121	1	49	200234	200234	NUM
ijassa-121	1	50	,	,	PUNCT
ijassa-121	1	51	china	china	PROPN
ijassa-121	1	52	scientific	scientific	PROPN
ijassa-121	1	53	computing	compute	VERB
ijassa-121	1	54	key	key	ADJ
ijassa-121	1	55	laboratory	laboratory	NOUN
ijassa-121	1	56	of	of	ADP
ijassa-121	1	57	shanghai	shanghai	PROPN
ijassa-121	1	58	universities	universities	PROPN
ijassa-121	1	59	,	,	PUNCT
ijassa-121	1	60	and	and	CCONJ
ijassa-121	1	61	division	division	NOUN
ijassa-121	1	62	of	of	ADP
ijassa-121	1	63	scientific	scientific	ADJ
ijassa-121	1	64	computation	computation	NOUN
ijassa-121	1	65	of	of	ADP
ijassa-121	1	66	e	e	NOUN
ijassa-121	1	67	-	-	NOUN
ijassa-121	1	68	institute	institute	NOUN
ijassa-121	1	69	of	of	ADP
ijassa-121	1	70	shanghai	shanghai	PROPN
ijassa-121	1	71	universities	university	NOUN
ijassa-121	1	72	abstract	abstract	VERB
ijassa-121	1	73	this	this	DET
ijassa-121	1	74	paper	paper	NOUN
ijassa-121	1	75	considers	consider	VERB
ijassa-121	1	76	optimal	optimal	ADJ
ijassa-121	1	77	designs	design	NOUN
ijassa-121	1	78	based	base	VERB
ijassa-121	1	79	on	on	ADP
ijassa-121	1	80	the	the	DET
ijassa-121	1	81	d-	d-	X
ijassa-121	1	82	,	,	PUNCT
ijassa-121	1	83	g-	g-	X
ijassa-121	1	84	,	,	PUNCT
ijassa-121	1	85	a-	a-	X
ijassa-121	1	86	,	,	PUNCT
ijassa-121	1	87	iand	iand	VERB
ijassa-121	1	88	ds	ds	ADJ
ijassa-121	1	89	-	-	PUNCT
ijassa-121	1	90	optimality	optimality	NOUN
ijassa-121	1	91	criteria	criterion	NOUN
ijassa-121	1	92	for	for	ADP
ijassa-121	1	93	a	a	DET
ijassa-121	1	94	random	random	ADJ
ijassa-121	1	95	intercept	intercept	NOUN
ijassa-121	1	96	model	model	NOUN
ijassa-121	1	97	with	with	ADP
ijassa-121	1	98	heteroscedastic	heteroscedastic	ADJ
ijassa-121	1	99	errors	error	NOUN
ijassa-121	1	100	.	.	PUNCT
ijassa-121	2	1	it	it	PRON
ijassa-121	2	2	is	be	AUX
ijassa-121	2	3	shown	show	VERB
ijassa-121	2	4	that	that	SCONJ
ijassa-121	2	5	the	the	DET
ijassa-121	2	6	search	search	NOUN
ijassa-121	2	7	of	of	ADP
ijassa-121	2	8	optimal	optimal	ADJ
ijassa-121	2	9	approximate	approximate	ADJ
ijassa-121	2	10	designs	design	NOUN
ijassa-121	2	11	can	can	AUX
ijassa-121	2	12	be	be	AUX
ijassa-121	2	13	confined	confine	VERB
ijassa-121	2	14	at	at	ADP
ijassa-121	2	15	extreme	extreme	ADJ
ijassa-121	2	16	settings	setting	NOUN
ijassa-121	2	17	of	of	ADP
ijassa-121	2	18	the	the	DET
ijassa-121	2	19	design	design	NOUN
ijassa-121	2	20	region	region	NOUN
ijassa-121	2	21	if	if	SCONJ
ijassa-121	2	22	heteroscedastic	heteroscedastic	ADJ
ijassa-121	2	23	structure	structure	NOUN
ijassa-121	2	24	satisfies	satisfie	NOUN
ijassa-121	2	25	specified	specify	VERB
ijassa-121	2	26	conditions	condition	NOUN
ijassa-121	2	27	.	.	PUNCT
ijassa-121	3	1	closed	close	VERB
ijassa-121	3	2	expressions	expression	NOUN
ijassa-121	3	3	for	for	ADP
ijassa-121	3	4	the	the	DET
ijassa-121	3	5	optimal	optimal	ADJ
ijassa-121	3	6	proportions	proportion	NOUN
ijassa-121	3	7	are	be	AUX
ijassa-121	3	8	given	give	VERB
ijassa-121	3	9	.	.	PUNCT
ijassa-121	4	1	keywords	keyword	NOUN
ijassa-121	4	2	optimal	optimal	ADJ
ijassa-121	4	3	design	design	NOUN
ijassa-121	4	4	,	,	PUNCT
ijassa-121	4	5	random	random	ADJ
ijassa-121	4	6	intercept	intercept	NOUN
ijassa-121	4	7	model	model	NOUN
ijassa-121	4	8	,	,	PUNCT
ijassa-121	4	9	heteroscedastic	heteroscedastic	ADJ
ijassa-121	4	10	errors	error	NOUN
ijassa-121	4	11	,	,	PUNCT
ijassa-121	4	12	identical	identical	ADJ
ijassa-121	4	13	design	design	NOUN
ijassa-121	4	14	1	1	NUM
ijassa-121	4	15	introduction	introduction	NOUN
ijassa-121	4	16	random	random	ADJ
ijassa-121	4	17	coefficient	coefficient	NOUN
ijassa-121	4	18	models	model	NOUN
ijassa-121	4	19	have	have	AUX
ijassa-121	4	20	been	be	AUX
ijassa-121	4	21	widely	widely	ADV
ijassa-121	4	22	used	use	VERB
ijassa-121	4	23	for	for	ADP
ijassa-121	4	24	the	the	DET
ijassa-121	4	25	researching	researching	NOUN
ijassa-121	4	26	in	in	ADP
ijassa-121	4	27	the	the	DET
ijassa-121	4	28	area	area	NOUN
ijassa-121	4	29	of	of	ADP
ijassa-121	4	30	biosciences	bioscience	NOUN
ijassa-121	4	31	,	,	PUNCT
ijassa-121	4	32	psychology	psychology	NOUN
ijassa-121	4	33	and	and	CCONJ
ijassa-121	4	34	population	population	NOUN
ijassa-121	4	35	pharmacokinetics	pharmacokinetic	NOUN
ijassa-121	4	36	,	,	PUNCT
ijassa-121	4	37	where	where	SCONJ
ijassa-121	4	38	repeated	repeat	VERB
ijassa-121	4	39	measurements	measurement	NOUN
ijassa-121	4	40	are	be	AUX
ijassa-121	4	41	available	available	ADJ
ijassa-121	4	42	from	from	ADP
ijassa-121	4	43	different	different	ADJ
ijassa-121	4	44	individuals	individual	NOUN
ijassa-121	4	45	.	.	PUNCT
ijassa-121	5	1	these	these	DET
ijassa-121	5	2	models	model	NOUN
ijassa-121	5	3	have	have	AUX
ijassa-121	5	4	been	be	AUX
ijassa-121	5	5	introduced	introduce	VERB
ijassa-121	5	6	by	by	ADP
ijassa-121	5	7	longford[1	longford[1	NOUN
ijassa-121	5	8	]	]	PUNCT
ijassa-121	5	9	,	,	PUNCT
ijassa-121	5	10	for	for	ADP
ijassa-121	5	11	recent	recent	ADJ
ijassa-121	5	12	researching	researching	NOUN
ijassa-121	5	13	we	we	PRON
ijassa-121	5	14	refer	refer	VERB
ijassa-121	5	15	to	to	ADP
ijassa-121	5	16	pena	pena	NOUN
ijassa-121	5	17	and	and	CCONJ
ijassa-121	5	18	yohai[2	yohai[2	NOUN
ijassa-121	5	19	]	]	X
ijassa-121	5	20	and	and	CCONJ
ijassa-121	5	21	yu[3	yu[3	NOUN
ijassa-121	5	22	]	]	PUNCT
ijassa-121	5	23	.	.	PUNCT
ijassa-121	6	1	in	in	ADP
ijassa-121	6	2	recent	recent	ADJ
ijassa-121	6	3	years	year	NOUN
ijassa-121	6	4	,	,	PUNCT
ijassa-121	6	5	the	the	DET
ijassa-121	6	6	problem	problem	NOUN
ijassa-121	6	7	of	of	ADP
ijassa-121	6	8	optimal	optimal	ADJ
ijassa-121	6	9	designs	design	NOUN
ijassa-121	6	10	for	for	ADP
ijassa-121	6	11	random	random	ADJ
ijassa-121	6	12	coefficient	coefficient	NOUN
ijassa-121	6	13	models	model	NOUN
ijassa-121	6	14	has	have	AUX
ijassa-121	6	15	attracted	attract	VERB
ijassa-121	6	16	growing	grow	VERB
ijassa-121	6	17	interest	interest	NOUN
ijassa-121	6	18	.	.	PUNCT
ijassa-121	7	1	schmelter[4	schmelter[4	NOUN
ijassa-121	7	2	-	-	SYM
ijassa-121	7	3	5	5	NUM
ijassa-121	7	4	]	]	PUNCT
ijassa-121	7	5	showed	show	VERB
ijassa-121	7	6	that	that	SCONJ
ijassa-121	7	7	optimal	optimal	ADJ
ijassa-121	7	8	designs	design	NOUN
ijassa-121	7	9	in	in	ADP
ijassa-121	7	10	the	the	DET
ijassa-121	7	11	linear	linear	ADJ
ijassa-121	7	12	mixed	mixed	ADJ
ijassa-121	7	13	models	model	NOUN
ijassa-121	7	14	could	could	AUX
ijassa-121	7	15	be	be	AUX
ijassa-121	7	16	restricted	restrict	VERB
ijassa-121	7	17	to	to	ADP
ijassa-121	7	18	the	the	DET
ijassa-121	7	19	class	class	NOUN
ijassa-121	7	20	of	of	ADP
ijassa-121	7	21	group	group	NOUN
ijassa-121	7	22	-	-	PUNCT
ijassa-121	7	23	wise	wise	ADJ
ijassa-121	7	24	identical	identical	ADJ
ijassa-121	7	25	designs	design	NOUN
ijassa-121	7	26	,	,	PUNCT
ijassa-121	7	27	and	and	CCONJ
ijassa-121	7	28	optimal	optimal	ADJ
ijassa-121	7	29	designs	design	NOUN
ijassa-121	7	30	in	in	ADP
ijassa-121	7	31	the	the	DET
ijassa-121	7	32	class	class	NOUN
ijassa-121	7	33	of	of	ADP
ijassa-121	7	34	single	single	ADJ
ijassa-121	7	35	-	-	PUNCT
ijassa-121	7	36	group	group	NOUN
ijassa-121	7	37	designs	design	NOUN
ijassa-121	7	38	were	be	AUX
ijassa-121	7	39	also	also	ADV
ijassa-121	7	40	optimal	optimal	ADJ
ijassa-121	7	41	designs	design	NOUN
ijassa-121	7	42	in	in	ADP
ijassa-121	7	43	the	the	DET
ijassa-121	7	44	larger	large	ADJ
ijassa-121	7	45	class	class	NOUN
ijassa-121	7	46	of	of	ADP
ijassa-121	7	47	more	more	ADJ
ijassa-121	7	48	group	group	NOUN
ijassa-121	7	49	designs	design	NOUN
ijassa-121	7	50	when	when	SCONJ
ijassa-121	7	51	the	the	DET
ijassa-121	7	52	design	design	NOUN
ijassa-121	7	53	criteria	criterion	NOUN
ijassa-121	7	54	satisfied	satisfy	VERB
ijassa-121	7	55	some	some	DET
ijassa-121	7	56	assumptions	assumption	NOUN
ijassa-121	7	57	.	.	PUNCT
ijassa-121	8	1	schwabe	schwabe	NOUN
ijassa-121	8	2	and	and	CCONJ
ijassa-121	8	3	schmelter[6	schmelter[6	PROPN
ijassa-121	8	4	]	]	X
ijassa-121	8	5	,	,	PUNCT
ijassa-121	8	6	schmelter	schmelter	NOUN
ijassa-121	8	7	et	et	PROPN
ijassa-121	8	8	al[7	al[7	PROPN
ijassa-121	8	9	]	]	PUNCT
ijassa-121	8	10	and	and	CCONJ
ijassa-121	8	11	luoma	luoma	VERB
ijassa-121	8	12	et	et	NOUN
ijassa-121	8	13	al[8	al[8	NOUN
ijassa-121	8	14	]	]	PUNCT
ijassa-121	8	15	.	.	PUNCT
ijassa-121	9	1	investigated	investigate	VERB
ijassa-121	9	2	optimal	optimal	ADJ
ijassa-121	9	3	designs	design	NOUN
ijassa-121	9	4	in	in	ADP
ijassa-121	9	5	random	random	ADJ
ijassa-121	9	6	intercept	intercept	NOUN
ijassa-121	9	7	model	model	NOUN
ijassa-121	9	8	,	,	PUNCT
ijassa-121	9	9	random	random	ADJ
ijassa-121	9	10	slope	slope	NOUN
ijassa-121	9	11	model	model	NOUN
ijassa-121	9	12	and	and	CCONJ
ijassa-121	9	13	random	random	ADJ
ijassa-121	9	14	coefficient	coefficient	NOUN
ijassa-121	9	15	cubic	cubic	ADJ
ijassa-121	9	16	regression	regression	NOUN
ijassa-121	9	17	model	model	NOUN
ijassa-121	9	18	,	,	PUNCT
ijassa-121	9	19	respectively	respectively	ADV
ijassa-121	9	20	.	.	PUNCT
ijassa-121	10	1	entholzner	entholzner	NOUN
ijassa-121	10	2	et	et	PROPN
ijassa-121	10	3	al[9	al[9	PROPN
ijassa-121	10	4	]	]	PUNCT
ijassa-121	10	5	obtained	obtain	VERB
ijassa-121	10	6	optimal	optimal	ADJ
ijassa-121	10	7	and	and	CCONJ
ijassa-121	10	8	efficient	efficient	ADJ
ijassa-121	10	9	designs	design	NOUN
ijassa-121	10	10	in	in	ADP
ijassa-121	10	11	mixed	mixed	ADJ
ijassa-121	10	12	models	model	NOUN
ijassa-121	10	13	.	.	PUNCT
ijassa-121	11	1	debusho	debusho	NOUN
ijassa-121	11	2	and	and	CCONJ
ijassa-121	11	3	haines[10	haines[10	NUM
ijassa-121	11	4	]	]	PUNCT
ijassa-121	11	5	provided	provide	VERB
ijassa-121	11	6	v	v	ADV
ijassa-121	11	7	-	-	ADJ
ijassa-121	11	8	optimal	optimal	ADJ
ijassa-121	11	9	and	and	CCONJ
ijassa-121	11	10	d	d	ADJ
ijassa-121	11	11	-	-	ADJ
ijassa-121	11	12	optimal	optimal	ADJ
ijassa-121	11	13	designs	design	NOUN
ijassa-121	11	14	with	with	ADP
ijassa-121	11	15	longitudinal	longitudinal	ADJ
ijassa-121	11	16	data	datum	NOUN
ijassa-121	11	17	in	in	ADP
ijassa-121	11	18	linear	linear	PROPN
ijassa-121	11	19	regression	regression	NOUN
ijassa-121	11	20	models	model	NOUN
ijassa-121	11	21	with	with	ADP
ijassa-121	11	22	a	a	DET
ijassa-121	11	23	random	random	ADJ
ijassa-121	11	24	intercept	intercept	NOUN
ijassa-121	11	25	.	.	PUNCT
ijassa-121	12	1	there	there	PRON
ijassa-121	12	2	are	be	VERB
ijassa-121	12	3	many	many	ADJ
ijassa-121	12	4	other	other	ADJ
ijassa-121	12	5	results	result	NOUN
ijassa-121	12	6	of	of	ADP
ijassa-121	12	7	optimal	optimal	ADJ
ijassa-121	12	8	designs	design	NOUN
ijassa-121	12	9	are	be	AUX
ijassa-121	12	10	obtained	obtain	VERB
ijassa-121	12	11	,	,	PUNCT
ijassa-121	12	12	such	such	ADJ
ijassa-121	12	13	as	as	ADP
ijassa-121	12	14	wang	wang	PROPN
ijassa-121	12	15	et	et	PROPN
ijassa-121	12	16	al[11	al[11	PROPN
ijassa-121	12	17	]	]	PUNCT
ijassa-121	12	18	,	,	PUNCT
ijassa-121	12	19	yu[12	yu[12	NOUN
ijassa-121	12	20	]	]	X
ijassa-121	12	21	and	and	CCONJ
ijassa-121	12	22	wen	wen	VERB
ijassa-121	12	23	et	et	NOUN
ijassa-121	12	24	al[13	al[13	PROPN
ijassa-121	12	25	]	]	PUNCT
ijassa-121	12	26	.	.	PUNCT
ijassa-121	13	1	in	in	ADP
ijassa-121	13	2	this	this	DET
ijassa-121	13	3	article	article	NOUN
ijassa-121	13	4	,	,	PUNCT
ijassa-121	13	5	we	we	PRON
ijassa-121	13	6	investigate	investigate	VERB
ijassa-121	13	7	the	the	DET
ijassa-121	13	8	problem	problem	NOUN
ijassa-121	13	9	of	of	ADP
ijassa-121	13	10	optimal	optimal	ADJ
ijassa-121	13	11	designs	design	NOUN
ijassa-121	13	12	based	base	VERB
ijassa-121	13	13	on	on	ADP
ijassa-121	13	14	some	some	DET
ijassa-121	13	15	common	common	ADJ
ijassa-121	13	16	optimality	optimality	NOUN
ijassa-121	13	17	criteria	criterion	NOUN
ijassa-121	13	18	for	for	ADP
ijassa-121	13	19	a	a	DET
ijassa-121	13	20	random	random	ADJ
ijassa-121	13	21	intercept	intercept	NOUN
ijassa-121	13	22	model	model	NOUN
ijassa-121	13	23	with	with	ADP
ijassa-121	13	24	heteroscedastic	heteroscedastic	ADJ
ijassa-121	13	25	errors	error	NOUN
ijassa-121	13	26	.	.	PUNCT
ijassa-121	14	1	in	in	ADP
ijassa-121	14	2	section	section	NOUN
ijassa-121	14	3	2	2	NUM
ijassa-121	14	4	,	,	PUNCT
ijassa-121	14	5	we	we	PRON
ijassa-121	14	6	introduce	introduce	VERB
ijassa-121	14	7	the	the	DET
ijassa-121	14	8	model	model	NOUN
ijassa-121	14	9	with	with	ADP
ijassa-121	14	10	necessary	necessary	ADJ
ijassa-121	14	11	notations	notation	NOUN
ijassa-121	14	12	.	.	PUNCT
ijassa-121	15	1	section	section	NOUN
ijassa-121	15	2	3	3	NUM
ijassa-121	15	3	provides	provide	VERB
ijassa-121	15	4	a	a	DET
ijassa-121	15	5	lemma	lemma	NOUN
ijassa-121	15	6	which	which	PRON
ijassa-121	15	7	makes	make	VERB
ijassa-121	15	8	it	it	PRON
ijassa-121	15	9	sure	sure	ADJ
ijassa-121	15	10	that	that	SCONJ
ijassa-121	15	11	we	we	PRON
ijassa-121	15	12	can	can	AUX
ijassa-121	15	13	confine	confine	VERB
ijassa-121	15	14	the	the	DET
ijassa-121	15	15	search	search	NOUN
ijassa-121	15	16	of	of	ADP
ijassa-121	15	17	optimal	optimal	ADJ
ijassa-121	15	18	designs	design	NOUN
ijassa-121	15	19	at	at	ADP
ijassa-121	15	20	extreme	extreme	ADJ
ijassa-121	15	21	settings	setting	NOUN
ijassa-121	15	22	of	of	ADP
ijassa-121	15	23	the	the	DET
ijassa-121	15	24	design	design	NOUN
ijassa-121	15	25	region	region	NOUN
ijassa-121	15	26	if	if	SCONJ
ijassa-121	15	27	the	the	DET
ijassa-121	15	28	optimality	optimality	NOUN
ijassa-121	15	29	criteria	criterion	NOUN
ijassa-121	15	30	satisfy	satisfy	VERB
ijassa-121	15	31	an	an	DET
ijassa-121	15	32	assumption	assumption	NOUN
ijassa-121	15	33	.	.	PUNCT
ijassa-121	16	1	simple	simple	ADJ
ijassa-121	16	2	expressions	expression	NOUN
ijassa-121	16	3	of	of	ADP
ijassa-121	16	4	these	these	DET
ijassa-121	16	5	optimal	optimal	ADJ
ijassa-121	16	6	advances	advance	NOUN
ijassa-121	16	7	in	in	ADP
ijassa-121	16	8	systems	system	NOUN
ijassa-121	16	9	science	science	NOUN
ijassa-121	16	10	and	and	CCONJ
ijassa-121	16	11	applications	application	NOUN
ijassa-121	16	12	(	(	PUNCT
ijassa-121	16	13	2012	2012	NUM
ijassa-121	16	14	)	)	PUNCT
ijassa-121	16	15	vol.12	vol.12	NOUN
ijassa-121	16	16	no.4	no.4	PROPN
ijassa-121	16	17	389	389	NUM
ijassa-121	16	18	designs	design	NOUN
ijassa-121	16	19	are	be	AUX
ijassa-121	16	20	given	give	VERB
ijassa-121	16	21	in	in	ADP
ijassa-121	16	22	this	this	DET
ijassa-121	16	23	section	section	NOUN
ijassa-121	16	24	.	.	PUNCT
ijassa-121	17	1	section	section	NOUN
ijassa-121	17	2	4	4	NUM
ijassa-121	17	3	introduces	introduce	VERB
ijassa-121	17	4	some	some	DET
ijassa-121	17	5	examples	example	NOUN
ijassa-121	17	6	.	.	PUNCT
ijassa-121	18	1	proof	proof	NOUN
ijassa-121	18	2	of	of	ADP
ijassa-121	18	3	lemma	lemma	PROPN
ijassa-121	18	4	1	1	NUM
ijassa-121	18	5	is	be	AUX
ijassa-121	18	6	given	give	VERB
ijassa-121	18	7	in	in	ADP
ijassa-121	18	8	appendix	appendix	NOUN
ijassa-121	18	9	.	.	PUNCT
ijassa-121	19	1	2	2	NUM
ijassa-121	19	2	the	the	DET
ijassa-121	19	3	random	random	ADJ
ijassa-121	19	4	intercept	intercept	NOUN
ijassa-121	19	5	model	model	NOUN
ijassa-121	19	6	with	with	ADP
ijassa-121	19	7	heteroscedastic	heteroscedastic	ADJ
ijassa-121	19	8	errors	error	NOUN
ijassa-121	19	9	we	we	PRON
ijassa-121	19	10	investigate	investigate	VERB
ijassa-121	19	11	a	a	DET
ijassa-121	19	12	linear	linear	ADJ
ijassa-121	19	13	regression	regression	NOUN
ijassa-121	19	14	model	model	NOUN
ijassa-121	19	15	on	on	ADP
ijassa-121	19	16	the	the	DET
ijassa-121	19	17	unit	unit	NOUN
ijassa-121	19	18	interval	interval	NOUN
ijassa-121	19	19	with	with	ADP
ijassa-121	19	20	a	a	DET
ijassa-121	19	21	random	random	ADJ
ijassa-121	19	22	intercept	intercept	NOUN
ijassa-121	19	23	and	and	CCONJ
ijassa-121	19	24	heteroscedastic	heteroscedastic	ADJ
ijassa-121	19	25	errors	error	NOUN
ijassa-121	19	26	.	.	PUNCT
ijassa-121	20	1	it	it	PRON
ijassa-121	20	2	is	be	AUX
ijassa-121	20	3	assumed	assume	VERB
ijassa-121	20	4	that	that	SCONJ
ijassa-121	20	5	there	there	PRON
ijassa-121	20	6	are	be	VERB
ijassa-121	20	7	individuals	individual	NOUN
ijassa-121	20	8	with	with	ADP
ijassa-121	20	9	observations	observation	NOUN
ijassa-121	20	10	each	each	PRON
ijassa-121	20	11	,	,	PUNCT
ijassa-121	20	12	and	and	CCONJ
ijassa-121	20	13	the	the	DET
ijassa-121	20	14	jth	jth	PROPN
ijassa-121	20	15	observation	observation	NOUN
ijassa-121	20	16	of	of	ADP
ijassa-121	20	17	ith	ith	PROPN
ijassa-121	20	18	individual	individual	NOUN
ijassa-121	20	19	is	be	AUX
ijassa-121	20	20	described	describe	VERB
ijassa-121	20	21	by	by	ADP
ijassa-121	20	22	yij	yij	NOUN
ijassa-121	20	23	=	=	SYM
ijassa-121	20	24	µi	µi	PROPN
ijassa-121	21	1	+	+	NUM
ijassa-121	21	2	xijβ	xijβ	PROPN
ijassa-121	21	3	+	+	CCONJ
ijassa-121	21	4	e(xij	e(xij	PROPN
ijassa-121	21	5	)	)	PUNCT
ijassa-121	21	6	,	,	PUNCT
ijassa-121	21	7	i	i	PRON
ijassa-121	21	8	=	=	NOUN
ijassa-121	21	9	1	1	NUM
ijassa-121	21	10	,	,	PUNCT
ijassa-121	21	11	...	...	PUNCT
ijassa-121	21	12	,	,	PUNCT
ijassa-121	21	13	n	n	CCONJ
ijassa-121	21	14	;	;	PUNCT
ijassa-121	21	15	j	j	PROPN
ijassa-121	21	16	=	=	SYM
ijassa-121	21	17	1	1	NUM
ijassa-121	21	18	,	,	PUNCT
ijassa-121	21	19	...	...	PUNCT
ijassa-121	21	20	,	,	PUNCT
ijassa-121	21	21	mi	mi	PROPN
ijassa-121	21	22	.	.	PROPN
ijassa-121	21	23	(	(	PUNCT
ijassa-121	21	24	1	1	NUM
ijassa-121	21	25	)	)	PUNCT
ijassa-121	21	26	where	where	SCONJ
ijassa-121	21	27	,	,	PUNCT
ijassa-121	21	28	xij	xij	PROPN
ijassa-121	21	29	∈	∈	PROPN
ijassa-121	22	1	[	[	X
ijassa-121	22	2	0	0	NUM
ijassa-121	22	3	,	,	PUNCT
ijassa-121	22	4	1	1	NUM
ijassa-121	22	5	]	]	PUNCT
ijassa-121	22	6	is	be	AUX
ijassa-121	22	7	the	the	DET
ijassa-121	22	8	experimental	experimental	ADJ
ijassa-121	22	9	setting	setting	NOUN
ijassa-121	22	10	;	;	PUNCT
ijassa-121	22	11	µi	µi	PROPN
ijassa-121	22	12	denotes	denote	VERB
ijassa-121	22	13	the	the	DET
ijassa-121	22	14	ith	ith	PROPN
ijassa-121	22	15	individual	individual	ADJ
ijassa-121	22	16	effect	effect	NOUN
ijassa-121	22	17	with	with	ADP
ijassa-121	22	18	unknown	unknown	ADJ
ijassa-121	22	19	mean	mean	NOUN
ijassa-121	22	20	µ	µ	ADP
ijassa-121	22	21	and	and	CCONJ
ijassa-121	22	22	known	know	VERB
ijassa-121	22	23	variance	variance	NOUN
ijassa-121	22	24	σ2	σ2	PROPN
ijassa-121	22	25	µ	µ	PROPN
ijassa-121	22	26	;	;	PUNCT
ijassa-121	22	27	β	β	X
ijassa-121	22	28	is	be	AUX
ijassa-121	22	29	the	the	DET
ijassa-121	22	30	unknown	unknown	ADJ
ijassa-121	22	31	slope	slope	NOUN
ijassa-121	22	32	parameter	parameter	NOUN
ijassa-121	22	33	;	;	PUNCT
ijassa-121	22	34	observational	observational	ADJ
ijassa-121	22	35	errors	error	NOUN
ijassa-121	22	36	e(xij	e(xij	PROPN
ijassa-121	22	37	)	)	PUNCT
ijassa-121	22	38	are	be	AUX
ijassa-121	22	39	assumed	assume	VERB
ijassa-121	22	40	to	to	PART
ijassa-121	22	41	be	be	AUX
ijassa-121	22	42	heteroscedastic	heteroscedastic	ADJ
ijassa-121	22	43	with	with	ADP
ijassa-121	22	44	zero	zero	NUM
ijassa-121	22	45	mean	mean	NOUN
ijassa-121	22	46	and	and	CCONJ
ijassa-121	22	47	variance	variance	NOUN
ijassa-121	22	48	σ2	σ2	PROPN
ijassa-121	22	49	/	/	SYM
ijassa-121	22	50	λ(xij	λ(xij	NOUN
ijassa-121	22	51	)	)	PUNCT
ijassa-121	22	52	,	,	PUNCT
ijassa-121	22	53	here	here	ADV
ijassa-121	22	54	σ2	σ2	PROPN
ijassa-121	22	55	is	be	AUX
ijassa-121	22	56	known	know	VERB
ijassa-121	22	57	and	and	CCONJ
ijassa-121	22	58	λ(xij	λ(xij	NOUN
ijassa-121	22	59	)	)	PUNCT
ijassa-121	22	60	is	be	AUX
ijassa-121	22	61	a	a	DET
ijassa-121	22	62	positive	positive	ADJ
ijassa-121	22	63	real	real	ADV
ijassa-121	22	64	-	-	PUNCT
ijassa-121	22	65	valued	value	VERB
ijassa-121	22	66	continuous	continuous	ADJ
ijassa-121	22	67	function	function	NOUN
ijassa-121	22	68	defined	define	VERB
ijassa-121	22	69	on	on	ADP
ijassa-121	22	70	[	[	X
ijassa-121	22	71	0,1	0,1	NUM
ijassa-121	22	72	]	]	PUNCT
ijassa-121	22	73	.	.	PUNCT
ijassa-121	23	1	we	we	PRON
ijassa-121	23	2	assume	assume	VERB
ijassa-121	23	3	that	that	NUM
ijassa-121	23	4	cov	cov	PROPN
ijassa-121	23	5	(	(	PUNCT
ijassa-121	23	6	µi	µi	PROPN
ijassa-121	23	7	,	,	PUNCT
ijassa-121	23	8	µi′	µi′	ADV
ijassa-121	23	9	)	)	PUNCT
ijassa-121	24	1	=	=	SYM
ijassa-121	24	2	0	0	NUM
ijassa-121	24	3	,	,	PUNCT
ijassa-121	24	4	i	i	PRON
ijassa-121	24	5	̸=	̸=	PROPN
ijassa-121	24	6	i′	i′	VERB
ijassa-121	24	7	cov	cov	NOUN
ijassa-121	24	8	(	(	PUNCT
ijassa-121	24	9	µi	µi	PROPN
ijassa-121	24	10	,	,	PUNCT
ijassa-121	24	11	e(xi′j	e(xi′j	ADJ
ijassa-121	24	12	)	)	PUNCT
ijassa-121	24	13	)	)	PUNCT
ijassa-121	25	1	=	=	SYM
ijassa-121	25	2	0	0	NUM
ijassa-121	25	3	,	,	PUNCT
ijassa-121	25	4	∀i	∀i	NOUN
ijassa-121	25	5	,	,	PUNCT
ijassa-121	25	6	i′	i′	NOUN
ijassa-121	25	7	;	;	PUNCT
ijassa-121	25	8	cov	cov	X
ijassa-121	25	9	(	(	PUNCT
ijassa-121	25	10	e(xij	e(xij	PROPN
ijassa-121	25	11	)	)	PUNCT
ijassa-121	25	12	,	,	PUNCT
ijassa-121	25	13	e(xi′j′	e(xi′j′	NOUN
ijassa-121	25	14	)	)	PUNCT
ijassa-121	25	15	)	)	PUNCT
ijassa-121	26	1	=	=	SYM
ijassa-121	26	2	0	0	NUM
ijassa-121	26	3	,	,	PUNCT
ijassa-121	26	4	(	(	PUNCT
ijassa-121	26	5	i	i	PROPN
ijassa-121	26	6	,	,	PUNCT
ijassa-121	26	7	j	j	PROPN
ijassa-121	26	8	)	)	PUNCT
ijassa-121	26	9	̸=	̸=	PROPN
ijassa-121	26	10	(	(	PUNCT
ijassa-121	26	11	i′	i′	NOUN
ijassa-121	26	12	,	,	PUNCT
ijassa-121	26	13	j′	j′	PROPN
ijassa-121	26	14	)	)	PUNCT
ijassa-121	26	15	.	.	PUNCT
ijassa-121	27	1	for	for	ADP
ijassa-121	27	2	the	the	DET
ijassa-121	27	3	ith	ith	PROPN
ijassa-121	27	4	individual	individual	NOUN
ijassa-121	27	5	,	,	PUNCT
ijassa-121	27	6	denote	denote	VERB
ijassa-121	27	7	yi	yi	NOUN
ijassa-121	28	1	=	=	PRON
ijassa-121	28	2			X
ijassa-121	28	3	yi1	yi1	NOUN
ijassa-121	28	4	...	...	PUNCT
ijassa-121	29	1	yimi	yimi	PROPN
ijassa-121	29	2			PROPN
ijassa-121	29	3	,	,	PUNCT
ijassa-121	29	4	xi	xi	PROPN
ijassa-121	29	5	=	=	PROPN
ijassa-121	30	1			X
ijassa-121	30	2	xi1	xi1	PROPN
ijassa-121	30	3	...	...	PUNCT
ijassa-121	31	1	ximi	ximi	PROPN
ijassa-121	31	2			PROPN
ijassa-121	31	3	,	,	PUNCT
ijassa-121	31	4	e(xi	e(xi	NUM
ijassa-121	31	5	)	)	PUNCT
ijassa-121	31	6	=	=	SYM
ijassa-121	31	7			X
ijassa-121	31	8	e(xi1	e(xi1	PROPN
ijassa-121	31	9	)	)	PUNCT
ijassa-121	31	10	...	...	PUNCT
ijassa-121	32	1	e(ximi	e(ximi	X
ijassa-121	32	2	)	)	PUNCT
ijassa-121	32	3			NOUN
ijassa-121	32	4	,	,	PUNCT
ijassa-121	32	5	fi	fi	NOUN
ijassa-121	32	6	=	=	NOUN
ijassa-121	32	7	(	(	PUNCT
ijassa-121	32	8	1mi	1mi	X
ijassa-121	32	9	,	,	PUNCT
ijassa-121	32	10	xi	xi	PROPN
ijassa-121	32	11	)	)	PUNCT
ijassa-121	32	12	here	here	ADV
ijassa-121	32	13	1mi	1mi	NOUN
ijassa-121	32	14	is	be	AUX
ijassa-121	32	15	a	a	DET
ijassa-121	32	16	vector	vector	NOUN
ijassa-121	32	17	of	of	ADP
ijassa-121	32	18	length	length	NOUN
ijassa-121	32	19	mi	mi	PROPN
ijassa-121	32	20	with	with	ADP
ijassa-121	32	21	all	all	DET
ijassa-121	32	22	entries	entry	NOUN
ijassa-121	32	23	equal	equal	ADJ
ijassa-121	32	24	one	one	NUM
ijassa-121	32	25	.	.	PUNCT
ijassa-121	33	1	then	then	ADV
ijassa-121	33	2	the	the	DET
ijassa-121	33	3	model	model	NOUN
ijassa-121	33	4	(	(	PUNCT
ijassa-121	33	5	1	1	X
ijassa-121	33	6	)	)	PUNCT
ijassa-121	33	7	can	can	AUX
ijassa-121	33	8	be	be	AUX
ijassa-121	33	9	expressed	express	VERB
ijassa-121	33	10	by	by	ADP
ijassa-121	33	11	yi	yi	PROPN
ijassa-121	33	12	=	=	NOUN
ijassa-121	33	13	fi	fi	NOUN
ijassa-121	33	14	(	(	PUNCT
ijassa-121	33	15	µ	µ	X
ijassa-121	33	16	β	β	X
ijassa-121	33	17	)	)	PUNCT
ijassa-121	34	1	+	+	CCONJ
ijassa-121	34	2	1mi(µi	1mi(µi	NUM
ijassa-121	34	3	−	−	PROPN
ijassa-121	34	4	µ	µ	NUM
ijassa-121	34	5	)	)	PUNCT
ijassa-121	34	6	+	+	NUM
ijassa-121	34	7	e(xi	e(xi	NUM
ijassa-121	34	8	)	)	PUNCT
ijassa-121	34	9	,	,	PUNCT
ijassa-121	34	10	fiθ	fiθ	NOUN
ijassa-121	34	11	+	+	X
ijassa-121	34	12	1mi(µi	1mi(µi	NUM
ijassa-121	34	13	−	−	PROPN
ijassa-121	34	14	µ	µ	NUM
ijassa-121	34	15	)	)	PUNCT
ijassa-121	34	16	+	+	NUM
ijassa-121	34	17	e(xi	e(xi	NUM
ijassa-121	34	18	)	)	PUNCT
ijassa-121	34	19	,	,	PUNCT
ijassa-121	34	20	i	i	PRON
ijassa-121	34	21	=	=	NOUN
ijassa-121	34	22	1	1	NUM
ijassa-121	34	23	,	,	PUNCT
ijassa-121	34	24	.	.	PUNCT
ijassa-121	34	25	.	.	PUNCT
ijassa-121	35	1	.	.	PUNCT
ijassa-121	36	1	,	,	PUNCT
ijassa-121	36	2	n.	n.	NOUN
ijassa-121	36	3	by	by	ADP
ijassa-121	36	4	the	the	DET
ijassa-121	36	5	assumptions	assumption	NOUN
ijassa-121	36	6	we	we	PRON
ijassa-121	36	7	have	have	VERB
ijassa-121	36	8	(	(	PUNCT
ijassa-121	36	9	µi	µi	INTJ
ijassa-121	36	10	−	−	PROPN
ijassa-121	36	11	µ	µ	NOUN
ijassa-121	36	12	)	)	PUNCT
ijassa-121	36	13	∼	∼	NOUN
ijassa-121	36	14	(	(	PUNCT
ijassa-121	36	15	0	0	NUM
ijassa-121	36	16	,	,	PUNCT
ijassa-121	36	17	σ2	σ2	NOUN
ijassa-121	36	18	µ	µ	NUM
ijassa-121	36	19	)	)	PUNCT
ijassa-121	36	20	and	and	CCONJ
ijassa-121	36	21	vi	vi	PROPN
ijassa-121	36	22	,	,	PUNCT
ijassa-121	36	23	cov	cov	PROPN
ijassa-121	36	24	(	(	PUNCT
ijassa-121	36	25	yi	yi	PROPN
ijassa-121	36	26	)	)	PUNCT
ijassa-121	36	27	=	=	PUNCT
ijassa-121	37	1	σ2diag{1	σ2diag{1	PROPN
ijassa-121	37	2	/	/	SYM
ijassa-121	37	3	λ(xi1	λ(xi1	NOUN
ijassa-121	37	4	)	)	PUNCT
ijassa-121	37	5	,	,	PUNCT
ijassa-121	37	6	.	.	PUNCT
ijassa-121	37	7	.	.	PUNCT
ijassa-121	38	1	.	.	PUNCT
ijassa-121	39	1	1	1	X
ijassa-121	39	2	/	/	SYM
ijassa-121	39	3	λ(ximi)}+σ2	λ(ximi)}+σ2	PROPN
ijassa-121	39	4	µ1mi1	µ1mi1	NOUN
ijassa-121	39	5	t	t	PROPN
ijassa-121	39	6	mi	mi	PROPN
ijassa-121	39	7	,	,	PUNCT
ijassa-121	39	8	σ2(di+d1mi1	σ2(di+d1mi1	PROPN
ijassa-121	39	9	t	t	PROPN
ijassa-121	39	10	mi	mi	PROPN
ijassa-121	39	11	)	)	PUNCT
ijassa-121	39	12	.	.	PUNCT
ijassa-121	40	1	here	here	ADV
ijassa-121	40	2	di	di	X
ijassa-121	40	3	=	=	PUNCT
ijassa-121	40	4	diag{1	diag{1	PROPN
ijassa-121	40	5	/	/	SYM
ijassa-121	40	6	λ(xi1	λ(xi1	PROPN
ijassa-121	40	7	)	)	PUNCT
ijassa-121	40	8	,	,	PUNCT
ijassa-121	40	9	.	.	PUNCT
ijassa-121	40	10	.	.	PUNCT
ijassa-121	41	1	.	.	PUNCT
ijassa-121	42	1	1	1	NUM
ijassa-121	42	2	/	/	SYM
ijassa-121	42	3	λ(ximi	λ(ximi	NOUN
ijassa-121	42	4	)	)	PUNCT
ijassa-121	42	5	}	}	PUNCT
ijassa-121	42	6	and	and	CCONJ
ijassa-121	42	7	d	d	PROPN
ijassa-121	42	8	=	=	SYM
ijassa-121	42	9	σ2	σ2	PROPN
ijassa-121	42	10	µ/σ	µ/σ	PROPN
ijassa-121	42	11	2	2	X
ijassa-121	42	12	.	.	PUNCT
ijassa-121	43	1	for	for	ADP
ijassa-121	43	2	all	all	PRON
ijassa-121	43	3	n	n	DET
ijassa-121	43	4	individuals	individual	NOUN
ijassa-121	43	5	,	,	PUNCT
ijassa-121	43	6	the	the	DET
ijassa-121	43	7	vector	vector	NOUN
ijassa-121	43	8	of	of	ADP
ijassa-121	43	9	all	all	DET
ijassa-121	43	10	observations	observation	NOUN
ijassa-121	43	11	can	can	AUX
ijassa-121	43	12	be	be	AUX
ijassa-121	43	13	expressed	express	VERB
ijassa-121	43	14	by	by	ADP
ijassa-121	43	15	y	y	PROPN
ijassa-121	43	16	=	=	PROPN
ijassa-121	44	1			NUM
ijassa-121	44	2	y1	y1	INTJ
ijassa-121	44	3	...	...	PUNCT
ijassa-121	45	1	yn	yn	PRON
ijassa-121	45	2			PROPN
ijassa-121	45	3	=	=	PRON
ijassa-121	45	4			PROPN
ijassa-121	45	5	f1	f1	PROPN
ijassa-121	45	6	...	...	PUNCT
ijassa-121	46	1	fn	fn	NOUN
ijassa-121	46	2			PROPN
ijassa-121	46	3	θ	θ	PROPN
ijassa-121	47	1	+	+	CCONJ
ijassa-121	47	2			X
ijassa-121	47	3	1m1	1m1	NUM
ijassa-121	47	4	0	0	NUM
ijassa-121	47	5	.	.	PUNCT
ijassa-121	47	6	.	.	PUNCT
ijassa-121	48	1	.	.	PUNCT
ijassa-121	48	2	0	0	NUM
ijassa-121	49	1	1mn	1mn	ADJ
ijassa-121	49	2			PROPN
ijassa-121	49	3			AUX
ijassa-121	49	4	µ1	µ1	PROPN
ijassa-121	49	5	−	−	PROPN
ijassa-121	49	6	µ	µ	X
ijassa-121	49	7	...	...	PUNCT
ijassa-121	49	8	µn	µn	PROPN
ijassa-121	49	9	−	−	PROPN
ijassa-121	49	10	µ	µ	X
ijassa-121	49	11	+	+	NOUN
ijassa-121	49	12			PROPN
ijassa-121	49	13	e(x1	e(x1	PROPN
ijassa-121	49	14	)	)	PUNCT
ijassa-121	49	15	...	...	PUNCT
ijassa-121	50	1	e(xn	e(xn	X
ijassa-121	50	2	)	)	PUNCT
ijassa-121	50	3			NOUN
ijassa-121	50	4	(	(	PUNCT
ijassa-121	50	5	2	2	NUM
ijassa-121	50	6	)	)	PUNCT
ijassa-121	50	7	390	390	NUM
ijassa-121	50	8	jing	jing	PROPN
ijassa-121	50	9	cheng	cheng	PROPN
ijassa-121	50	10	:	:	PUNCT
ijassa-121	50	11	optimal	optimal	ADJ
ijassa-121	50	12	designs	design	NOUN
ijassa-121	50	13	in	in	ADP
ijassa-121	50	14	random	random	ADJ
ijassa-121	50	15	intercept	intercept	NOUN
ijassa-121	50	16	model	model	NOUN
ijassa-121	50	17	with	with	ADP
ijassa-121	50	18	heteroscedastic	heteroscedastic	ADJ
ijassa-121	50	19	errors	error	NOUN
ijassa-121	50	20	the	the	DET
ijassa-121	50	21	design	design	NOUN
ijassa-121	50	22	matrix	matrix	NOUN
ijassa-121	50	23	for	for	ADP
ijassa-121	50	24	random	random	ADJ
ijassa-121	50	25	intercepts	intercept	NOUN
ijassa-121	50	26	is	be	AUX
ijassa-121	50	27	block	block	NOUN
ijassa-121	50	28	diagonal	diagonal	ADJ
ijassa-121	50	29	,	,	PUNCT
ijassa-121	50	30	e.g.	e.g.	ADV
ijassa-121	50	31	,	,	PUNCT
ijassa-121	50	32	1m1	1m1	NUM
ijassa-121	50	33	0	0	NUM
ijassa-121	50	34	.	.	PUNCT
ijassa-121	50	35	.	.	PUNCT
ijassa-121	51	1	.	.	PUNCT
ijassa-121	51	2	0	0	NUM
ijassa-121	52	1	1mn	1mn	ADJ
ijassa-121	52	2			PROPN
ijassa-121	52	3	.	.	PUNCT
ijassa-121	53	1	consequently	consequently	ADV
ijassa-121	53	2	,	,	PUNCT
ijassa-121	53	3	the	the	DET
ijassa-121	53	4	covariance	covariance	NOUN
ijassa-121	53	5	matrix	matrix	NOUN
ijassa-121	53	6	of	of	ADP
ijassa-121	53	7	y	y	PROPN
ijassa-121	53	8	is	be	AUX
ijassa-121	53	9	cov	cov	PROPN
ijassa-121	53	10	(	(	PUNCT
ijassa-121	53	11	y	y	PROPN
ijassa-121	53	12	)	)	PUNCT
ijassa-121	53	13	=	=	PUNCT
ijassa-121	53	14	diag{v1	diag{v1	NOUN
ijassa-121	53	15	,	,	PUNCT
ijassa-121	53	16	.	.	PUNCT
ijassa-121	53	17	.	.	PUNCT
ijassa-121	53	18	.	.	PUNCT
ijassa-121	54	1	,	,	PUNCT
ijassa-121	54	2	vn	vn	PROPN
ijassa-121	54	3	}	}	PUNCT
ijassa-121	54	4	.	.	PUNCT
ijassa-121	55	1	the	the	DET
ijassa-121	55	2	best	good	ADJ
ijassa-121	55	3	linear	linear	ADJ
ijassa-121	55	4	unbiased	unbiased	ADJ
ijassa-121	55	5	estimate	estimate	NOUN
ijassa-121	55	6	of	of	ADP
ijassa-121	55	7	θ	θ	PROPN
ijassa-121	55	8	is	be	AUX
ijassa-121	55	9	given	give	VERB
ijassa-121	55	10	by	by	ADP
ijassa-121	55	11	θ̂	θ̂	NOUN
ijassa-121	55	12	=	=	SYM
ijassa-121	56	1	(	(	PUNCT
ijassa-121	56	2	n∑	n∑	NOUN
ijassa-121	56	3	i=1	i=1	PROPN
ijassa-121	57	1	f	f	PROPN
ijassa-121	57	2	t	t	PROPN
ijassa-121	58	1	i	i	PRON
ijassa-121	58	2	v	v	VERB
ijassa-121	58	3	−1	−1	NOUN
ijassa-121	59	1	i	i	PRON
ijassa-121	59	2	fi	fi	NOUN
ijassa-121	59	3	)	)	PUNCT
ijassa-121	60	1	−1	−1	NOUN
ijassa-121	60	2	n∑	n∑	NOUN
ijassa-121	61	1	i=1	i=1	PROPN
ijassa-121	62	1	f	f	PROPN
ijassa-121	62	2	t	t	PROPN
ijassa-121	63	1	i	i	PRON
ijassa-121	63	2	v	v	VERB
ijassa-121	63	3	−1	−1	NOUN
ijassa-121	64	1	i	i	PRON
ijassa-121	64	2	yi	yi	INTJ
ijassa-121	64	3	.	.	PUNCT
ijassa-121	65	1	(	(	PUNCT
ijassa-121	65	2	3	3	X
ijassa-121	65	3	)	)	PUNCT
ijassa-121	65	4	and	and	CCONJ
ijassa-121	65	5	we	we	PRON
ijassa-121	65	6	can	can	AUX
ijassa-121	65	7	get	get	VERB
ijassa-121	65	8	cov	cov	NOUN
ijassa-121	65	9	(	(	PUNCT
ijassa-121	65	10	θ̂	θ̂	NUM
ijassa-121	65	11	)	)	PUNCT
ijassa-121	65	12	=	=	SYM
ijassa-121	66	1	(	(	PUNCT
ijassa-121	66	2	n∑	n∑	NOUN
ijassa-121	66	3	i=1	i=1	PROPN
ijassa-121	67	1	f	f	PROPN
ijassa-121	67	2	t	t	PROPN
ijassa-121	68	1	i	i	PRON
ijassa-121	68	2	v	v	VERB
ijassa-121	68	3	−1	−1	NOUN
ijassa-121	69	1	i	i	PRON
ijassa-121	69	2	fi	fi	NOUN
ijassa-121	69	3	)	)	PUNCT
ijassa-121	69	4	−1	−1	NOUN
ijassa-121	69	5	.	.	PUNCT
ijassa-121	70	1	3	3	NUM
ijassa-121	70	2	optimal	optimal	ADJ
ijassa-121	70	3	designs	design	NOUN
ijassa-121	70	4	in	in	ADP
ijassa-121	70	5	this	this	DET
ijassa-121	70	6	section	section	NOUN
ijassa-121	70	7	,	,	PUNCT
ijassa-121	70	8	we	we	PRON
ijassa-121	70	9	investigate	investigate	VERB
ijassa-121	70	10	the	the	DET
ijassa-121	70	11	optimal	optimal	ADJ
ijassa-121	70	12	designs	design	NOUN
ijassa-121	70	13	based	base	VERB
ijassa-121	70	14	on	on	ADP
ijassa-121	70	15	d-	d-	X
ijassa-121	70	16	,	,	PUNCT
ijassa-121	70	17	g-	g-	X
ijassa-121	70	18	,	,	PUNCT
ijassa-121	70	19	a-	a-	X
ijassa-121	70	20	,	,	PUNCT
ijassa-121	70	21	iand	iand	NOUN
ijassa-121	70	22	dsoptimality	dsoptimality	NOUN
ijassa-121	70	23	criteria	criterion	NOUN
ijassa-121	70	24	for	for	ADP
ijassa-121	70	25	the	the	DET
ijassa-121	70	26	models	model	NOUN
ijassa-121	70	27	described	describe	VERB
ijassa-121	70	28	in	in	ADP
ijassa-121	70	29	previous	previous	ADJ
ijassa-121	70	30	section	section	NOUN
ijassa-121	70	31	.	.	PUNCT
ijassa-121	71	1	the	the	DET
ijassa-121	71	2	d	d	ADJ
ijassa-121	71	3	-	-	ADJ
ijassa-121	71	4	optimal	optimal	ADJ
ijassa-121	71	5	design	design	NOUN
ijassa-121	71	6	minimizes	minimize	VERB
ijassa-121	71	7	the	the	DET
ijassa-121	71	8	generalized	generalized	ADJ
ijassa-121	71	9	variance	variance	NOUN
ijassa-121	71	10	of	of	ADP
ijassa-121	71	11	parameter	parameter	NOUN
ijassa-121	71	12	estimates	estimate	NOUN
ijassa-121	71	13	,	,	PUNCT
ijassa-121	71	14	the	the	DET
ijassa-121	71	15	g	g	NOUN
ijassa-121	71	16	-	-	PUNCT
ijassa-121	71	17	optimal	optimal	ADJ
ijassa-121	71	18	design	design	NOUN
ijassa-121	71	19	minimizes	minimize	VERB
ijassa-121	71	20	the	the	DET
ijassa-121	71	21	maximum	maximum	ADJ
ijassa-121	71	22	variance	variance	NOUN
ijassa-121	71	23	of	of	ADP
ijassa-121	71	24	the	the	DET
ijassa-121	71	25	predicted	predict	VERB
ijassa-121	71	26	value	value	NOUN
ijassa-121	71	27	of	of	ADP
ijassa-121	71	28	the	the	DET
ijassa-121	71	29	response	response	NOUN
ijassa-121	71	30	over	over	ADP
ijassa-121	71	31	the	the	DET
ijassa-121	71	32	design	design	NOUN
ijassa-121	71	33	region	region	NOUN
ijassa-121	71	34	,	,	PUNCT
ijassa-121	71	35	the	the	DET
ijassa-121	71	36	a	a	ADV
ijassa-121	71	37	-	-	PUNCT
ijassa-121	71	38	optimal	optimal	ADJ
ijassa-121	71	39	design	design	NOUN
ijassa-121	71	40	minimizes	minimize	VERB
ijassa-121	71	41	the	the	DET
ijassa-121	71	42	total	total	ADJ
ijassa-121	71	43	variance	variance	NOUN
ijassa-121	71	44	of	of	ADP
ijassa-121	71	45	the	the	DET
ijassa-121	71	46	parameter	parameter	NOUN
ijassa-121	71	47	estimates	estimate	NOUN
ijassa-121	71	48	,	,	PUNCT
ijassa-121	71	49	the	the	DET
ijassa-121	71	50	i	i	NOUN
ijassa-121	71	51	-	-	PUNCT
ijassa-121	71	52	optimal	optimal	ADJ
ijassa-121	71	53	design	design	NOUN
ijassa-121	71	54	minimizes	minimize	VERB
ijassa-121	71	55	the	the	DET
ijassa-121	71	56	integrated	integrate	VERB
ijassa-121	71	57	mean	mean	NOUN
ijassa-121	71	58	squared	square	VERB
ijassa-121	71	59	error	error	NOUN
ijassa-121	71	60	and	and	CCONJ
ijassa-121	71	61	the	the	DET
ijassa-121	71	62	interest	interest	NOUN
ijassa-121	71	63	of	of	ADP
ijassa-121	71	64	ds	ds	ADJ
ijassa-121	71	65	-	-	PUNCT
ijassa-121	71	66	optimal	optimal	ADJ
ijassa-121	71	67	design	design	NOUN
ijassa-121	71	68	is	be	AUX
ijassa-121	71	69	in	in	ADP
ijassa-121	71	70	estimating	estimate	VERB
ijassa-121	71	71	the	the	DET
ijassa-121	71	72	slope	slope	NOUN
ijassa-121	71	73	.	.	PUNCT
ijassa-121	72	1	in	in	ADP
ijassa-121	72	2	some	some	DET
ijassa-121	72	3	practical	practical	ADJ
ijassa-121	72	4	situations	situation	NOUN
ijassa-121	72	5	like	like	ADP
ijassa-121	72	6	human	human	ADJ
ijassa-121	72	7	or	or	CCONJ
ijassa-121	72	8	animal	animal	NOUN
ijassa-121	72	9	pharmaceutics	pharmaceutic	NOUN
ijassa-121	72	10	studies	study	NOUN
ijassa-121	72	11	or	or	CCONJ
ijassa-121	72	12	medical	medical	ADJ
ijassa-121	72	13	diagnostics	diagnostic	NOUN
ijassa-121	72	14	there	there	PRON
ijassa-121	72	15	are	be	VERB
ijassa-121	72	16	often	often	ADV
ijassa-121	72	17	restrictions	restriction	NOUN
ijassa-121	72	18	,	,	PUNCT
ijassa-121	72	19	e.g.	e.g.	ADV
ijassa-121	72	20	,	,	PUNCT
ijassa-121	72	21	technical	technical	ADJ
ijassa-121	72	22	implementations	implementation	NOUN
ijassa-121	72	23	,	,	PUNCT
ijassa-121	72	24	which	which	PRON
ijassa-121	72	25	force	force	VERB
ijassa-121	72	26	the	the	DET
ijassa-121	72	27	experiment	experiment	NOUN
ijassa-121	72	28	to	to	PART
ijassa-121	72	29	be	be	AUX
ijassa-121	72	30	performed	perform	VERB
ijassa-121	72	31	with	with	ADP
ijassa-121	72	32	identical	identical	ADJ
ijassa-121	72	33	regimes	regime	NOUN
ijassa-121	72	34	for	for	ADP
ijassa-121	72	35	all	all	DET
ijassa-121	72	36	individuals	individual	NOUN
ijassa-121	72	37	.	.	PUNCT
ijassa-121	73	1	this	this	PRON
ijassa-121	73	2	means	mean	VERB
ijassa-121	73	3	that	that	SCONJ
ijassa-121	73	4	for	for	ADP
ijassa-121	73	5	each	each	DET
ijassa-121	73	6	individual	individual	NOUN
ijassa-121	73	7	the	the	DET
ijassa-121	73	8	number	number	NOUN
ijassa-121	73	9	mi	mi	NOUN
ijassa-121	73	10	of	of	ADP
ijassa-121	73	11	repeated	repeat	VERB
ijassa-121	73	12	measurements	measurement	NOUN
ijassa-121	73	13	equals	equal	VERB
ijassa-121	73	14	m	m	PRON
ijassa-121	73	15	and	and	CCONJ
ijassa-121	73	16	experimental	experimental	ADJ
ijassa-121	73	17	settings	setting	NOUN
ijassa-121	73	18	xij	xij	PROPN
ijassa-121	73	19	=	=	SYM
ijassa-121	73	20	xj	xj	NOUN
ijassa-121	73	21	are	be	AUX
ijassa-121	73	22	identical	identical	ADJ
ijassa-121	73	23	across	across	ADP
ijassa-121	73	24	all	all	DET
ijassa-121	73	25	the	the	DET
ijassa-121	73	26	individuals	individual	NOUN
ijassa-121	73	27	.	.	PUNCT
ijassa-121	74	1	so	so	ADV
ijassa-121	74	2	we	we	PRON
ijassa-121	74	3	only	only	ADV
ijassa-121	74	4	consider	consider	VERB
ijassa-121	74	5	identical	identical	ADJ
ijassa-121	74	6	designs	design	NOUN
ijassa-121	74	7	in	in	ADP
ijassa-121	74	8	the	the	DET
ijassa-121	74	9	following	following	NOUN
ijassa-121	74	10	,	,	PUNCT
ijassa-121	74	11	i.e.	i.e.	X
ijassa-121	74	12	,	,	PUNCT
ijassa-121	74	13	mi	mi	PROPN
ijassa-121	74	14	=	=	PROPN
ijassa-121	74	15	m	m	PROPN
ijassa-121	74	16	,	,	PUNCT
ijassa-121	74	17	xi	xi	X
ijassa-121	75	1	=	=	PUNCT
ijassa-121	75	2	x1	x1	PROPN
ijassa-121	75	3	and	and	CCONJ
ijassa-121	75	4	hence	hence	ADV
ijassa-121	75	5	,	,	PUNCT
ijassa-121	75	6	fi	fi	NOUN
ijassa-121	75	7	=	=	SYM
ijassa-121	75	8	f1	f1	NOUN
ijassa-121	75	9	,	,	PUNCT
ijassa-121	75	10	vi	vi	NOUN
ijassa-121	75	11	=	=	SYM
ijassa-121	75	12	v	v	NOUN
ijassa-121	75	13	for	for	ADP
ijassa-121	75	14	all	all	DET
ijassa-121	75	15	i.	i.	NOUN
ijassa-121	75	16	then	then	ADV
ijassa-121	75	17	the	the	DET
ijassa-121	75	18	best	good	ADJ
ijassa-121	75	19	linear	linear	ADJ
ijassa-121	75	20	unbiased	unbiased	ADJ
ijassa-121	75	21	estimate	estimate	NOUN
ijassa-121	75	22	of	of	ADP
ijassa-121	75	23	θ	θ	PROPN
ijassa-121	75	24	can	can	AUX
ijassa-121	75	25	be	be	AUX
ijassa-121	75	26	written	write	VERB
ijassa-121	75	27	as	as	ADP
ijassa-121	75	28	θ̂	θ̂	X
ijassa-121	75	29	=	=	SYM
ijassa-121	75	30	(	(	PUNCT
ijassa-121	75	31	nf	nf	INTJ
ijassa-121	75	32	t	t	PROPN
ijassa-121	75	33	1	1	NUM
ijassa-121	75	34	v	v	NUM
ijassa-121	75	35	−1	−1	NOUN
ijassa-121	75	36	1	1	NUM
ijassa-121	75	37	f1	f1	NOUN
ijassa-121	75	38	)	)	PUNCT
ijassa-121	75	39	−1	−1	NOUN
ijassa-121	76	1	f	f	PROPN
ijassa-121	76	2	t	t	PROPN
ijassa-121	76	3	1	1	NUM
ijassa-121	76	4	v	v	NUM
ijassa-121	76	5	−1	−1	NOUN
ijassa-121	76	6	1	1	NUM
ijassa-121	76	7	n∑	n∑	NOUN
ijassa-121	76	8	i=1	i=1	PROPN
ijassa-121	76	9	yi	yi	PROPN
ijassa-121	76	10	.	.	PUNCT
ijassa-121	77	1	here	here	ADV
ijassa-121	77	2	v	v	ADP
ijassa-121	77	3	−1	−1	NOUN
ijassa-121	77	4	1	1	NUM
ijassa-121	77	5	=	=	SYM
ijassa-121	77	6	1	1	NUM
ijassa-121	77	7	σ2	σ2	PROPN
ijassa-121	77	8	(	(	PUNCT
ijassa-121	77	9	d1	d1	PROPN
ijassa-121	77	10	+	+	CCONJ
ijassa-121	77	11	d1m1tm	d1m1tm	PROPN
ijassa-121	77	12	)	)	PUNCT
ijassa-121	77	13	−1	−1	NOUN
ijassa-121	77	14	=	=	SYM
ijassa-121	77	15	1	1	NUM
ijassa-121	77	16	σ2	σ2	PROPN
ijassa-121	77	17	(	(	PUNCT
ijassa-121	77	18	d−1	d−1	PROPN
ijassa-121	77	19	1	1	NUM
ijassa-121	77	20	−	−	NOUN
ijassa-121	77	21	dd−1	dd−1	NOUN
ijassa-121	77	22	1	1	NUM
ijassa-121	77	23	1m1tmd−1	1m1tmd−1	NUM
ijassa-121	77	24	1	1	NUM
ijassa-121	77	25	1	1	NUM
ijassa-121	77	26	+	+	CCONJ
ijassa-121	77	27	d1tmd−1	d1tmd−1	PROPN
ijassa-121	77	28	1	1	NUM
ijassa-121	77	29	1	1	NUM
ijassa-121	77	30	m	m	NOUN
ijassa-121	77	31	)	)	PUNCT
ijassa-121	77	32	=	=	SYM
ijassa-121	77	33	1	1	NUM
ijassa-121	77	34	σ2	σ2	PROPN
ijassa-121	77	35	(	(	PUNCT
ijassa-121	77	36	diag{λ(xj	diag{λ(xj	NOUN
ijassa-121	77	37	)	)	PUNCT
ijassa-121	77	38	}	}	PUNCT
ijassa-121	77	39	−	−	ADP
ijassa-121	78	1	dd−1	dd−1	NOUN
ijassa-121	78	2	1	1	NUM
ijassa-121	78	3	1m1tmd−1	1m1tmd−1	NUM
ijassa-121	78	4	1	1	NUM
ijassa-121	78	5	1	1	NUM
ijassa-121	78	6	+	+	CCONJ
ijassa-121	78	7	d	d	NOUN
ijassa-121	78	8	∑n	∑n	PROPN
ijassa-121	78	9	j=1	j=1	PROPN
ijassa-121	78	10	λ(xj	λ(xj	PROPN
ijassa-121	78	11	)	)	PUNCT
ijassa-121	78	12	)	)	PUNCT
ijassa-121	78	13	.	.	PUNCT
ijassa-121	79	1	advances	advance	NOUN
ijassa-121	79	2	in	in	ADP
ijassa-121	79	3	systems	system	NOUN
ijassa-121	79	4	science	science	NOUN
ijassa-121	79	5	and	and	CCONJ
ijassa-121	79	6	applications	application	NOUN
ijassa-121	79	7	(	(	PUNCT
ijassa-121	79	8	2012	2012	NUM
ijassa-121	79	9	)	)	PUNCT
ijassa-121	79	10	vol.12	vol.12	NOUN
ijassa-121	79	11	no.4	no.4	PROPN
ijassa-121	79	12	391	391	NUM
ijassa-121	79	13	without	without	ADP
ijassa-121	79	14	loss	loss	NOUN
ijassa-121	79	15	of	of	ADP
ijassa-121	79	16	generality	generality	NOUN
ijassa-121	79	17	,	,	PUNCT
ijassa-121	79	18	we	we	PRON
ijassa-121	79	19	assume	assume	VERB
ijassa-121	79	20	σ2	σ2	NOUN
ijassa-121	79	21	=	=	NOUN
ijassa-121	79	22	1	1	NUM
ijassa-121	79	23	in	in	ADP
ijassa-121	79	24	the	the	DET
ijassa-121	79	25	followings	following	NOUN
ijassa-121	79	26	.	.	PUNCT
ijassa-121	80	1	furthermore	furthermore	ADV
ijassa-121	80	2	,	,	PUNCT
ijassa-121	80	3	we	we	PRON
ijassa-121	80	4	will	will	AUX
ijassa-121	80	5	consider	consider	VERB
ijassa-121	80	6	approximate	approximate	ADJ
ijassa-121	80	7	designs	design	NOUN
ijassa-121	80	8	.	.	PUNCT
ijassa-121	81	1	for	for	ADP
ijassa-121	81	2	any	any	DET
ijassa-121	81	3	approximate	approximate	ADJ
ijassa-121	81	4	design	design	NOUN
ijassa-121	81	5	ξ	ξ	PROPN
ijassa-121	81	6	of	of	ADP
ijassa-121	81	7	the	the	DET
ijassa-121	81	8	following	follow	VERB
ijassa-121	81	9	form	form	NOUN
ijassa-121	81	10	ξ	ξ	PROPN
ijassa-121	81	11	=	=	SYM
ijassa-121	81	12	(	(	PUNCT
ijassa-121	81	13	x1	x1	PROPN
ijassa-121	81	14	,	,	PUNCT
ijassa-121	81	15	.	.	PUNCT
ijassa-121	81	16	.	.	PUNCT
ijassa-121	81	17	.	.	PUNCT
ijassa-121	82	1	,	,	PUNCT
ijassa-121	82	2	xp	xp	PROPN
ijassa-121	82	3	ω1	ω1	PROPN
ijassa-121	82	4	,	,	PUNCT
ijassa-121	82	5	.	.	PUNCT
ijassa-121	82	6	.	.	PUNCT
ijassa-121	83	1	.	.	PUNCT
ijassa-121	84	1	,	,	PUNCT
ijassa-121	84	2	ωp	ωp	NOUN
ijassa-121	84	3	)	)	PUNCT
ijassa-121	84	4	,	,	PUNCT
ijassa-121	84	5	2	2	NUM
ijassa-121	84	6	<	<	X
ijassa-121	84	7	p	p	X
ijassa-121	84	8	<	<	X
ijassa-121	84	9	m	m	PROPN
ijassa-121	84	10	,	,	PUNCT
ijassa-121	84	11	p∑	p∑	X
ijassa-121	84	12	j=1	j=1	NOUN
ijassa-121	84	13	ωj	ωj	ADP
ijassa-121	84	14	=	=	NOUN
ijassa-121	84	15	1	1	X
ijassa-121	84	16	.	.	PUNCT
ijassa-121	85	1	(	(	PUNCT
ijassa-121	85	2	4	4	X
ijassa-121	85	3	)	)	PUNCT
ijassa-121	85	4	denote	denote	NOUN
ijassa-121	85	5	νk	νk	NOUN
ijassa-121	85	6	=	=	SYM
ijassa-121	85	7	∫	∫	PROPN
ijassa-121	85	8	1	1	NUM
ijassa-121	85	9	0	0	NUM
ijassa-121	85	10	xkλ(x)dξ(x	xkλ(x)dξ(x	NUM
ijassa-121	85	11	)	)	PUNCT
ijassa-121	86	1	=	=	PUNCT
ijassa-121	86	2	p∑	p∑	NOUN
ijassa-121	87	1	j=1	j=1	PROPN
ijassa-121	87	2	ωjx	ωjx	VERB
ijassa-121	87	3	k	k	PROPN
ijassa-121	87	4	jλ(xj	jλ(xj	PROPN
ijassa-121	87	5	)	)	PUNCT
ijassa-121	87	6	,	,	PUNCT
ijassa-121	87	7	k	k	PROPN
ijassa-121	88	1	=	=	SYM
ijassa-121	88	2	0	0	NUM
ijassa-121	88	3	,	,	PUNCT
ijassa-121	88	4	1	1	NUM
ijassa-121	88	5	,	,	PUNCT
ijassa-121	88	6	2	2	NUM
ijassa-121	88	7	.	.	PUNCT
ijassa-121	89	1	then	then	ADV
ijassa-121	89	2	the	the	DET
ijassa-121	89	3	information	information	NOUN
ijassa-121	89	4	matrix	matrix	NOUN
ijassa-121	89	5	corresponding	correspond	VERB
ijassa-121	89	6	to	to	ADP
ijassa-121	89	7	the	the	DET
ijassa-121	89	8	design	design	NOUN
ijassa-121	89	9	ξ	ξ	PROPN
ijassa-121	89	10	of	of	ADP
ijassa-121	89	11	the	the	DET
ijassa-121	89	12	form	form	NOUN
ijassa-121	89	13	(	(	PUNCT
ijassa-121	89	14	4	4	X
ijassa-121	89	15	)	)	PUNCT
ijassa-121	89	16	can	can	AUX
ijassa-121	89	17	be	be	AUX
ijassa-121	89	18	expressed	express	VERB
ijassa-121	89	19	by	by	ADP
ijassa-121	89	20	m(ξ	m(ξ	NOUN
ijassa-121	89	21	)	)	PUNCT
ijassa-121	89	22	=	=	SYM
ijassa-121	89	23	mn	mn	PROPN
ijassa-121	89	24	1	1	NUM
ijassa-121	90	1	+	+	CCONJ
ijassa-121	90	2	γν0	γν0	NOUN
ijassa-121	90	3	(	(	PUNCT
ijassa-121	90	4	ν0	ν0	PROPN
ijassa-121	90	5	ν1	ν1	NOUN
ijassa-121	90	6	ν1	ν1	NOUN
ijassa-121	90	7	ν2	ν2	NOUN
ijassa-121	90	8	+	+	CCONJ
ijassa-121	90	9	γ(ν0ν2	γ(ν0ν2	PROPN
ijassa-121	90	10	−	−	NOUN
ijassa-121	90	11	ν21	ν21	NOUN
ijassa-121	90	12	)	)	PUNCT
ijassa-121	90	13	)	)	PUNCT
ijassa-121	90	14	.	.	PUNCT
ijassa-121	91	1	(	(	PUNCT
ijassa-121	91	2	5	5	X
ijassa-121	91	3	)	)	PUNCT
ijassa-121	91	4	here	here	ADV
ijassa-121	91	5	we	we	PRON
ijassa-121	91	6	note	note	VERB
ijassa-121	91	7	γ	γ	X
ijassa-121	91	8	=	=	SYM
ijassa-121	91	9	md	md	PROPN
ijassa-121	91	10	.	.	PROPN
ijassa-121	92	1	for	for	ADP
ijassa-121	92	2	regression	regression	NOUN
ijassa-121	92	3	model	model	NOUN
ijassa-121	92	4	without	without	ADP
ijassa-121	92	5	any	any	DET
ijassa-121	92	6	random	random	ADJ
ijassa-121	92	7	effects	effect	NOUN
ijassa-121	92	8	,	,	PUNCT
ijassa-121	92	9	optimal	optimal	ADJ
ijassa-121	92	10	designs	design	NOUN
ijassa-121	92	11	are	be	AUX
ijassa-121	92	12	obtained	obtain	VERB
ijassa-121	92	13	at	at	ADP
ijassa-121	92	14	extreme	extreme	ADJ
ijassa-121	92	15	settings	setting	NOUN
ijassa-121	92	16	of	of	ADP
ijassa-121	92	17	the	the	DET
ijassa-121	92	18	design	design	NOUN
ijassa-121	92	19	region	region	NOUN
ijassa-121	92	20	and	and	CCONJ
ijassa-121	92	21	schwabe	schwabe	NOUN
ijassa-121	92	22	et	et	PROPN
ijassa-121	92	23	al[6	al[6	PROPN
ijassa-121	92	24	]	]	PUNCT
ijassa-121	92	25	discussed	discuss	VERB
ijassa-121	92	26	optimal	optimal	ADJ
ijassa-121	92	27	designs	design	NOUN
ijassa-121	92	28	of	of	ADP
ijassa-121	92	29	random	random	ADJ
ijassa-121	92	30	intercept	intercept	NOUN
ijassa-121	92	31	models	model	NOUN
ijassa-121	92	32	.	.	PUNCT
ijassa-121	93	1	we	we	PRON
ijassa-121	93	2	ca	can	AUX
ijassa-121	93	3	n’t	not	PART
ijassa-121	93	4	use	use	VERB
ijassa-121	93	5	the	the	DET
ijassa-121	93	6	conclusions	conclusion	NOUN
ijassa-121	93	7	in	in	ADP
ijassa-121	93	8	schwabe	schwabe	PROPN
ijassa-121	93	9	et	et	PROPN
ijassa-121	93	10	al[6	al[6	PROPN
ijassa-121	93	11	]	]	PUNCT
ijassa-121	93	12	directly	directly	ADV
ijassa-121	93	13	in	in	ADP
ijassa-121	93	14	the	the	DET
ijassa-121	93	15	random	random	ADJ
ijassa-121	93	16	intercept	intercept	NOUN
ijassa-121	93	17	model	model	NOUN
ijassa-121	93	18	with	with	ADP
ijassa-121	93	19	heteroscedastic	heteroscedastic	ADJ
ijassa-121	93	20	errors	error	NOUN
ijassa-121	93	21	,	,	PUNCT
ijassa-121	93	22	but	but	CCONJ
ijassa-121	93	23	we	we	PRON
ijassa-121	93	24	have	have	VERB
ijassa-121	93	25	the	the	DET
ijassa-121	93	26	following	follow	VERB
ijassa-121	93	27	lemma	lemma	PROPN
ijassa-121	93	28	.	.	PUNCT
ijassa-121	94	1	lemma	lemma	PROPN
ijassa-121	94	2	1	1	NUM
ijassa-121	94	3	in	in	ADP
ijassa-121	94	4	the	the	DET
ijassa-121	94	5	model	model	NOUN
ijassa-121	94	6	(	(	PUNCT
ijassa-121	94	7	2	2	NUM
ijassa-121	94	8	)	)	PUNCT
ijassa-121	94	9	,	,	PUNCT
ijassa-121	94	10	assume	assume	VERB
ijassa-121	94	11	that	that	SCONJ
ijassa-121	94	12	mi	mi	PROPN
ijassa-121	94	13	=	=	PROPN
ijassa-121	94	14	m	m	PROPN
ijassa-121	94	15	,	,	PUNCT
ijassa-121	94	16	(	(	PUNCT
ijassa-121	94	17	i	i	NOUN
ijassa-121	94	18	=	=	NOUN
ijassa-121	94	19	1	1	NUM
ijassa-121	94	20	,	,	PUNCT
ijassa-121	94	21	.	.	PUNCT
ijassa-121	94	22	.	.	PUNCT
ijassa-121	95	1	.	.	PUNCT
ijassa-121	95	2	,	,	PUNCT
ijassa-121	96	1	n	n	CCONJ
ijassa-121	96	2	)	)	PUNCT
ijassa-121	97	1	and	and	CCONJ
ijassa-121	97	2	λ(x	λ(x	PROPN
ijassa-121	97	3	)	)	PUNCT
ijassa-121	97	4	satisfies	satisfy	VERB
ijassa-121	97	5	the	the	DET
ijassa-121	97	6	following	follow	VERB
ijassa-121	97	7	condition	condition	NOUN
ijassa-121	97	8	1	1	NUM
ijassa-121	97	9	λ(x	λ(x	PROPN
ijassa-121	97	10	)	)	PUNCT
ijassa-121	97	11	≥	≥	NOUN
ijassa-121	97	12	1−	1−	NUM
ijassa-121	97	13	x	x	SYM
ijassa-121	97	14	λ0	λ0	NOUN
ijassa-121	97	15	+	+	NOUN
ijassa-121	97	16	x	x	SYM
ijassa-121	97	17	λ(1	λ(1	PROPN
ijassa-121	97	18	)	)	PUNCT
ijassa-121	97	19	,	,	PUNCT
ijassa-121	97	20	x	x	PUNCT
ijassa-121	97	21	∈	∈	PROPN
ijassa-121	98	1	[	[	X
ijassa-121	98	2	0	0	NUM
ijassa-121	98	3	,	,	PUNCT
ijassa-121	98	4	1	1	NUM
ijassa-121	98	5	]	]	PUNCT
ijassa-121	98	6	.	.	PUNCT
ijassa-121	99	1	(	(	PUNCT
ijassa-121	99	2	6	6	NUM
ijassa-121	99	3	)	)	PUNCT
ijassa-121	99	4	where	where	SCONJ
ijassa-121	99	5	λ0	λ0	NOUN
ijassa-121	99	6	=	=	SYM
ijassa-121	99	7	λ(0	λ(0	NOUN
ijassa-121	99	8	)	)	PUNCT
ijassa-121	99	9	and	and	CCONJ
ijassa-121	99	10	λ1	λ1	PROPN
ijassa-121	99	11	=	=	SYM
ijassa-121	99	12	λ(1	λ(1	PROPN
ijassa-121	99	13	)	)	PUNCT
ijassa-121	99	14	.	.	PUNCT
ijassa-121	100	1	then	then	ADV
ijassa-121	100	2	for	for	ADP
ijassa-121	100	3	any	any	DET
ijassa-121	100	4	approximate	approximate	ADJ
ijassa-121	100	5	design	design	NOUN
ijassa-121	100	6	ξ	ξ	PROPN
ijassa-121	100	7	of	of	ADP
ijassa-121	100	8	the	the	DET
ijassa-121	100	9	form	form	NOUN
ijassa-121	100	10	(	(	PUNCT
ijassa-121	100	11	4	4	NUM
ijassa-121	100	12	)	)	PUNCT
ijassa-121	100	13	,	,	PUNCT
ijassa-121	100	14	there	there	PRON
ijassa-121	100	15	exists	exist	VERB
ijassa-121	100	16	an	an	DET
ijassa-121	100	17	approximate	approximate	ADJ
ijassa-121	100	18	design	design	NOUN
ijassa-121	100	19	of	of	ADP
ijassa-121	100	20	the	the	DET
ijassa-121	100	21	form	form	NOUN
ijassa-121	100	22	ξ∗	ξ∗	NOUN
ijassa-121	100	23	=	=	SYM
ijassa-121	100	24	(	(	PUNCT
ijassa-121	100	25	0	0	NUM
ijassa-121	100	26	,	,	PUNCT
ijassa-121	100	27	1	1	NUM
ijassa-121	100	28	1−	1−	NUM
ijassa-121	100	29	ω	ω	NUM
ijassa-121	100	30	,	,	PUNCT
ijassa-121	100	31	ω	ω	PROPN
ijassa-121	100	32	)	)	PUNCT
ijassa-121	100	33	,	,	PUNCT
ijassa-121	100	34	0	0	NUM
ijassa-121	100	35	<	<	X
ijassa-121	100	36	ω	ω	X
ijassa-121	100	37	<	<	X
ijassa-121	100	38	1	1	NUM
ijassa-121	100	39	.	.	PUNCT
ijassa-121	100	40	such	such	ADJ
ijassa-121	100	41	that	that	DET
ijassa-121	100	42	m(ξ∗	m(ξ∗	NOUN
ijassa-121	100	43	)	)	PUNCT
ijassa-121	100	44	≥	≥	NOUN
ijassa-121	100	45	m(ξ	m(ξ	NOUN
ijassa-121	100	46	)	)	PUNCT
ijassa-121	100	47	.	.	PUNCT
ijassa-121	101	1	the	the	DET
ijassa-121	101	2	proof	proof	NOUN
ijassa-121	101	3	of	of	ADP
ijassa-121	101	4	lemma	lemma	PROPN
ijassa-121	101	5	1	1	NUM
ijassa-121	101	6	can	can	AUX
ijassa-121	101	7	be	be	AUX
ijassa-121	101	8	found	find	VERB
ijassa-121	101	9	in	in	ADP
ijassa-121	101	10	the	the	DET
ijassa-121	101	11	appendix	appendix	NOUN
ijassa-121	101	12	.	.	PUNCT
ijassa-121	102	1	the	the	DET
ijassa-121	102	2	criteria	criterion	NOUN
ijassa-121	102	3	,	,	PUNCT
ijassa-121	102	4	φ	φ	X
ijassa-121	102	5	(	(	PUNCT
ijassa-121	102	6	·	·	PUNCT
ijassa-121	102	7	)	)	PUNCT
ijassa-121	102	8	,	,	PUNCT
ijassa-121	102	9	considered	consider	VERB
ijassa-121	102	10	in	in	ADP
ijassa-121	102	11	this	this	DET
ijassa-121	102	12	paper	paper	NOUN
ijassa-121	102	13	are	be	AUX
ijassa-121	102	14	functions	function	NOUN
ijassa-121	102	15	of	of	ADP
ijassa-121	102	16	the	the	DET
ijassa-121	102	17	information	information	NOUN
ijassa-121	102	18	matrices	matrix	NOUN
ijassa-121	102	19	which	which	PRON
ijassa-121	102	20	are	be	AUX
ijassa-121	102	21	required	require	VERB
ijassa-121	102	22	to	to	PART
ijassa-121	102	23	satisfy	satisfy	VERB
ijassa-121	102	24	the	the	DET
ijassa-121	102	25	following	follow	VERB
ijassa-121	102	26	assumptions	assumption	NOUN
ijassa-121	102	27	:	:	PUNCT
ijassa-121	102	28	a1	a1	NOUN
ijassa-121	102	29	φ	φ	PROPN
ijassa-121	102	30	(	(	PUNCT
ijassa-121	102	31	·	·	PUNCT
ijassa-121	102	32	)	)	PUNCT
ijassa-121	102	33	,	,	PUNCT
ijassa-121	102	34	is	be	AUX
ijassa-121	102	35	a	a	DET
ijassa-121	102	36	real	real	ADV
ijassa-121	102	37	-	-	PUNCT
ijassa-121	102	38	valued	value	VERB
ijassa-121	102	39	function	function	NOUN
ijassa-121	102	40	defined	define	VERB
ijassa-121	102	41	on	on	ADP
ijassa-121	102	42	the	the	DET
ijassa-121	102	43	whole	whole	ADJ
ijassa-121	102	44	set	set	NOUN
ijassa-121	102	45	m	m	NOUN
ijassa-121	102	46	of	of	ADP
ijassa-121	102	47	2×2	2×2	NUM
ijassa-121	102	48	symmetric	symmetric	ADJ
ijassa-121	102	49	non	non	ADJ
ijassa-121	102	50	-	-	ADJ
ijassa-121	102	51	negative	negative	ADJ
ijassa-121	102	52	definite	definite	ADJ
ijassa-121	102	53	matrices	matrix	NOUN
ijassa-121	102	54	,	,	PUNCT
ijassa-121	102	55	φ	φ	PROPN
ijassa-121	102	56	:	:	PUNCT
ijassa-121	102	57	m	m	VERB
ijassa-121	102	58	→	→	PUNCT
ijassa-121	102	59	(	(	PUNCT
ijassa-121	102	60	−∞,∞	−∞,∞	NOUN
ijassa-121	102	61	]	]	X
ijassa-121	102	62	;	;	PUNCT
ijassa-121	102	63	a2	a2	PROPN
ijassa-121	102	64	φ	φ	PROPN
ijassa-121	102	65	(	(	PUNCT
ijassa-121	102	66	·	·	PUNCT
ijassa-121	102	67	)	)	PUNCT
ijassa-121	102	68	is	be	AUX
ijassa-121	102	69	monotone	monotone	ADJ
ijassa-121	102	70	(	(	PUNCT
ijassa-121	102	71	the	the	DET
ijassa-121	102	72	loewner	loewner	ADJ
ijassa-121	102	73	order	order	NOUN
ijassa-121	102	74	(	(	PUNCT
ijassa-121	102	75	e.g.	e.g.	ADV
ijassa-121	102	76	,	,	PUNCT
ijassa-121	102	77	pukelsheim[14	pukelsheim[14	NOUN
ijassa-121	102	78	]	]	PUNCT
ijassa-121	102	79	,	,	PUNCT
ijassa-121	102	80	p.101	p.101	NOUN
ijassa-121	102	81	)	)	PUNCT
ijassa-121	102	82	)	)	PUNCT
ijassa-121	102	83	on	on	ADP
ijassa-121	102	84	m	m	NOUN
ijassa-121	102	85	in	in	ADP
ijassa-121	102	86	the	the	DET
ijassa-121	102	87	sense	sense	NOUN
ijassa-121	102	88	that	that	SCONJ
ijassa-121	102	89	m1,m2	m1,m2	PROPN
ijassa-121	102	90	∈	∈	PROPN
ijassa-121	102	91	m	m	PROPN
ijassa-121	102	92	,	,	PUNCT
ijassa-121	102	93	m∞	m∞	X
ijassa-121	102	94	≥	≥	NUM
ijassa-121	102	95	m∈	m∈	PROPN
ijassa-121	102	96	⇒	⇒	PROPN
ijassa-121	102	97	⊕(m∞	⊕(m∞	NOUN
ijassa-121	102	98	)	)	PUNCT
ijassa-121	102	99	≤	≤	NOUN
ijassa-121	102	100	⊕(m∈	⊕(m∈	NUM
ijassa-121	102	101	)	)	PUNCT
ijassa-121	102	102	.	.	PUNCT
ijassa-121	103	1	392	392	NUM
ijassa-121	103	2	jing	jing	ADJ
ijassa-121	103	3	cheng	cheng	PROPN
ijassa-121	103	4	:	:	PUNCT
ijassa-121	103	5	optimal	optimal	ADJ
ijassa-121	103	6	designs	design	NOUN
ijassa-121	103	7	in	in	ADP
ijassa-121	103	8	random	random	ADJ
ijassa-121	103	9	intercept	intercept	NOUN
ijassa-121	103	10	model	model	NOUN
ijassa-121	103	11	with	with	ADP
ijassa-121	103	12	heteroscedastic	heteroscedastic	ADJ
ijassa-121	103	13	errors	error	NOUN
ijassa-121	103	14	these	these	DET
ijassa-121	103	15	assumptions	assumption	NOUN
ijassa-121	103	16	are	be	AUX
ijassa-121	103	17	satisfied	satisfied	ADJ
ijassa-121	103	18	for	for	SCONJ
ijassa-121	103	19	most	most	ADJ
ijassa-121	103	20	of	of	ADP
ijassa-121	103	21	the	the	DET
ijassa-121	103	22	common	common	ADJ
ijassa-121	103	23	criteria	criterion	NOUN
ijassa-121	103	24	including	include	VERB
ijassa-121	103	25	the	the	DET
ijassa-121	103	26	d-	d-	X
ijassa-121	103	27	,	,	PUNCT
ijassa-121	103	28	g-	g-	X
ijassa-121	103	29	,	,	PUNCT
ijassa-121	103	30	a-	a-	X
ijassa-121	103	31	,	,	PUNCT
ijassa-121	103	32	iand	iand	VERB
ijassa-121	103	33	ds	ds	ADJ
ijassa-121	103	34	-	-	PUNCT
ijassa-121	103	35	optimality	optimality	NOUN
ijassa-121	103	36	.	.	PUNCT
ijassa-121	104	1	so	so	ADV
ijassa-121	104	2	,	,	PUNCT
ijassa-121	104	3	by	by	ADP
ijassa-121	104	4	majorization	majorization	NOUN
ijassa-121	104	5	we	we	PRON
ijassa-121	104	6	can	can	AUX
ijassa-121	104	7	confine	confine	VERB
ijassa-121	104	8	the	the	DET
ijassa-121	104	9	search	search	NOUN
ijassa-121	104	10	of	of	ADP
ijassa-121	104	11	optimal	optimal	ADJ
ijassa-121	104	12	designs	design	NOUN
ijassa-121	104	13	at	at	ADP
ijassa-121	104	14	extreme	extreme	ADJ
ijassa-121	104	15	settings	setting	NOUN
ijassa-121	104	16	x	x	PUNCT
ijassa-121	105	1	=	=	SYM
ijassa-121	105	2	0	0	NUM
ijassa-121	105	3	and	and	CCONJ
ijassa-121	105	4	x	x	SYM
ijassa-121	105	5	=	=	SYM
ijassa-121	105	6	1	1	NUM
ijassa-121	105	7	if	if	SCONJ
ijassa-121	105	8	λ(x	λ(x	X
ijassa-121	105	9	)	)	PUNCT
ijassa-121	105	10	satisfies	satisfy	VERB
ijassa-121	105	11	the	the	DET
ijassa-121	105	12	condition	condition	NOUN
ijassa-121	105	13	(	(	PUNCT
ijassa-121	105	14	6	6	NUM
ijassa-121	105	15	)	)	PUNCT
ijassa-121	105	16	in	in	ADP
ijassa-121	105	17	lemma	lemma	PROPN
ijassa-121	105	18	1	1	NUM
ijassa-121	105	19	.	.	PUNCT
ijassa-121	106	1	therefore	therefore	ADV
ijassa-121	106	2	,	,	PUNCT
ijassa-121	106	3	in	in	ADP
ijassa-121	106	4	what	what	PRON
ijassa-121	106	5	follows	follow	VERB
ijassa-121	106	6	we	we	PRON
ijassa-121	106	7	only	only	ADV
ijassa-121	106	8	consider	consider	VERB
ijassa-121	106	9	approximate	approximate	ADJ
ijassa-121	106	10	designs	design	NOUN
ijassa-121	106	11	ξ	ξ	PROPN
ijassa-121	106	12	of	of	ADP
ijassa-121	106	13	the	the	DET
ijassa-121	106	14	form	form	NOUN
ijassa-121	106	15	ξ	ξ	X
ijassa-121	106	16	=	=	SYM
ijassa-121	106	17	(	(	PUNCT
ijassa-121	106	18	0	0	NUM
ijassa-121	106	19	1	1	NUM
ijassa-121	106	20	1−	1−	NUM
ijassa-121	106	21	ω	ω	NUM
ijassa-121	106	22	ω	ω	PROPN
ijassa-121	106	23	)	)	PUNCT
ijassa-121	106	24	.	.	PUNCT
ijassa-121	107	1	(	(	PUNCT
ijassa-121	107	2	7	7	X
ijassa-121	107	3	)	)	PUNCT
ijassa-121	107	4	for	for	ADP
ijassa-121	107	5	approximate	approximate	ADJ
ijassa-121	107	6	designs	design	NOUN
ijassa-121	107	7	of	of	ADP
ijassa-121	107	8	the	the	DET
ijassa-121	107	9	form	form	NOUN
ijassa-121	107	10	(	(	PUNCT
ijassa-121	107	11	7	7	NUM
ijassa-121	107	12	)	)	PUNCT
ijassa-121	107	13	,	,	PUNCT
ijassa-121	107	14	we	we	PRON
ijassa-121	107	15	have	have	VERB
ijassa-121	107	16	ν0	ν0	PROPN
ijassa-121	107	17	=	=	PUNCT
ijassa-121	108	1	λ1ω	λ1ω	X
ijassa-121	108	2	+	+	NUM
ijassa-121	108	3	λ0(1−	λ0(1−	PROPN
ijassa-121	108	4	ω	ω	NUM
ijassa-121	108	5	)	)	PUNCT
ijassa-121	108	6	,	,	PUNCT
ijassa-121	108	7	ν1	ν1	NOUN
ijassa-121	108	8	=	=	SYM
ijassa-121	108	9	ν2	ν2	PROPN
ijassa-121	108	10	=	=	SYM
ijassa-121	108	11	λ1ω	λ1ω	NOUN
ijassa-121	108	12	.	.	PUNCT
ijassa-121	109	1	first	first	ADV
ijassa-121	109	2	,	,	PUNCT
ijassa-121	109	3	we	we	PRON
ijassa-121	109	4	consider	consider	VERB
ijassa-121	109	5	the	the	DET
ijassa-121	109	6	d	d	NOUN
ijassa-121	109	7	-	-	PUNCT
ijassa-121	109	8	optimality	optimality	NOUN
ijassa-121	109	9	φ(m(ξ	φ(m(ξ	NOUN
ijassa-121	109	10	)	)	PUNCT
ijassa-121	109	11	)	)	PUNCT
ijassa-121	110	1	=	=	PUNCT
ijassa-121	110	2	∣∣m−1(ξ	∣∣m−1(ξ	NOUN
ijassa-121	110	3	)	)	PUNCT
ijassa-121	110	4	∣∣.	∣∣.	PROPN
ijassa-121	110	5	note	note	VERB
ijassa-121	110	6	that	that	SCONJ
ijassa-121	110	7	|m(ξ)|	|m(ξ)|	NOUN
ijassa-121	110	8	,	,	PUNCT
ijassa-121	110	9	|m(ω)|	|m(ω)|	PROPN
ijassa-121	110	10	=	=	SYM
ijassa-121	110	11	(	(	PUNCT
ijassa-121	110	12	mn)2λ0λ1ω(1−	mn)2λ0λ1ω(1−	PROPN
ijassa-121	110	13	ω	ω	NOUN
ijassa-121	110	14	)	)	PUNCT
ijassa-121	110	15	1	1	NUM
ijassa-121	111	1	+	+	CCONJ
ijassa-121	111	2	γ[ωλ1	γ[ωλ1	X
ijassa-121	111	3	+	+	CCONJ
ijassa-121	111	4	(	(	PUNCT
ijassa-121	111	5	1−	1−	NUM
ijassa-121	111	6	ω)λ0	ω)λ0	NOUN
ijassa-121	111	7	]	]	PUNCT
ijassa-121	111	8	.	.	PUNCT
ijassa-121	112	1	it	it	PRON
ijassa-121	112	2	is	be	AUX
ijassa-121	112	3	easy	easy	ADJ
ijassa-121	112	4	to	to	PART
ijassa-121	112	5	verify	verify	VERB
ijassa-121	112	6	that	that	SCONJ
ijassa-121	112	7	|m(ω)|	|m(ω)|	PROPN
ijassa-121	112	8	is	be	AUX
ijassa-121	112	9	maximized	maximize	VERB
ijassa-121	112	10	at	at	ADP
ijassa-121	112	11	ω	ω	PROPN
ijassa-121	112	12	=	=	NOUN
ijassa-121	112	13	√	√	PROPN
ijassa-121	112	14	1	1	NUM
ijassa-121	112	15	+	+	X
ijassa-121	112	16	γλ0/	γλ0/	PROPN
ijassa-121	112	17	(	(	PUNCT
ijassa-121	112	18	√	√	ADV
ijassa-121	112	19	1	1	NUM
ijassa-121	112	20	+	+	CCONJ
ijassa-121	112	21	γλ0	γλ0	NOUN
ijassa-121	113	1	+	+	CCONJ
ijassa-121	113	2	√	√	NUM
ijassa-121	113	3	1	1	NUM
ijassa-121	113	4	+	+	CCONJ
ijassa-121	113	5	γλ1	γλ1	NOUN
ijassa-121	113	6	)	)	PUNCT
ijassa-121	113	7	and	and	CCONJ
ijassa-121	113	8	hence	hence	ADV
ijassa-121	113	9	∣∣m−1(ω	∣∣m−1(ω	NOUN
ijassa-121	113	10	)	)	PUNCT
ijassa-121	113	11	∣∣	∣∣	NUM
ijassa-121	113	12	is	be	AUX
ijassa-121	113	13	minimized	minimize	VERB
ijassa-121	113	14	.	.	PUNCT
ijassa-121	114	1	therefore	therefore	ADV
ijassa-121	114	2	we	we	PRON
ijassa-121	114	3	have	have	AUX
ijassa-121	114	4	theorem	theorem	VERB
ijassa-121	114	5	1	1	NUM
ijassa-121	114	6	for	for	ADP
ijassa-121	114	7	the	the	DET
ijassa-121	114	8	model	model	NOUN
ijassa-121	114	9	(	(	PUNCT
ijassa-121	114	10	2	2	NUM
ijassa-121	114	11	)	)	PUNCT
ijassa-121	114	12	with	with	ADP
ijassa-121	114	13	mi	mi	PROPN
ijassa-121	115	1	=	=	VERB
ijassa-121	115	2	m	m	PROPN
ijassa-121	115	3	(	(	PUNCT
ijassa-121	115	4	i	i	NOUN
ijassa-121	115	5	=	=	NOUN
ijassa-121	115	6	1	1	NUM
ijassa-121	115	7	,	,	PUNCT
ijassa-121	115	8	.	.	PUNCT
ijassa-121	115	9	.	.	PUNCT
ijassa-121	115	10	.	.	PUNCT
ijassa-121	115	11	,	,	PUNCT
ijassa-121	115	12	n	n	CCONJ
ijassa-121	115	13	)	)	PUNCT
ijassa-121	115	14	and	and	CCONJ
ijassa-121	115	15	λ(x	λ(x	ADJ
ijassa-121	115	16	)	)	PUNCT
ijassa-121	115	17	satisfying	satisfy	VERB
ijassa-121	115	18	(	(	PUNCT
ijassa-121	115	19	6	6	NUM
ijassa-121	115	20	)	)	PUNCT
ijassa-121	115	21	the	the	DET
ijassa-121	115	22	d	d	ADJ
ijassa-121	115	23	-	-	ADJ
ijassa-121	115	24	optimal	optimal	ADJ
ijassa-121	115	25	design	design	NOUN
ijassa-121	115	26	is	be	AUX
ijassa-121	115	27	ξ∗d	ξ∗d	NOUN
ijassa-121	115	28	=	=	SYM
ijassa-121	115	29	(	(	PUNCT
ijassa-121	115	30	0	0	NUM
ijassa-121	115	31	,	,	PUNCT
ijassa-121	115	32	1	1	NUM
ijassa-121	115	33	1−	1−	NUM
ijassa-121	115	34	ωd	ωd	NOUN
ijassa-121	115	35	,	,	PUNCT
ijassa-121	115	36	ωd	ωd	NUM
ijassa-121	115	37	)	)	PUNCT
ijassa-121	115	38	,	,	PUNCT
ijassa-121	115	39	ωd	ωd	X
ijassa-121	115	40	=	=	NOUN
ijassa-121	115	41	√	√	NUM
ijassa-121	115	42	1	1	NUM
ijassa-121	116	1	+	+	CCONJ
ijassa-121	116	2	γλ0√	γλ0√	ADJ
ijassa-121	116	3	1	1	NUM
ijassa-121	117	1	+	+	NOUN
ijassa-121	117	2	γλ0	γλ0	NOUN
ijassa-121	118	1	+	+	ADJ
ijassa-121	118	2	√	√	NUM
ijassa-121	118	3	1	1	NUM
ijassa-121	118	4	+	+	NUM
ijassa-121	118	5	γλ1	γλ1	NOUN
ijassa-121	118	6	for	for	ADP
ijassa-121	118	7	the	the	DET
ijassa-121	118	8	ds	ds	ADJ
ijassa-121	118	9	-	-	PUNCT
ijassa-121	118	10	optimality	optimality	NOUN
ijassa-121	118	11	,	,	PUNCT
ijassa-121	118	12	note	note	VERB
ijassa-121	118	13	that	that	SCONJ
ijassa-121	118	14	the	the	DET
ijassa-121	118	15	covariance	covariance	NOUN
ijassa-121	118	16	matrix	matrix	NOUN
ijassa-121	118	17	of	of	ADP
ijassa-121	118	18	θ̂	θ̂	PUNCT
ijassa-121	118	19	can	can	AUX
ijassa-121	118	20	be	be	AUX
ijassa-121	118	21	calculated	calculate	VERB
ijassa-121	118	22	by	by	ADP
ijassa-121	118	23	m−1(ω	m−1(ω	PROPN
ijassa-121	118	24	)	)	PUNCT
ijassa-121	119	1	=	=	SYM
ijassa-121	119	2	1	1	X
ijassa-121	119	3	mn(ν0	mn(ν0	VERB
ijassa-121	119	4	−	−	PROPN
ijassa-121	119	5	ν2	ν2	PROPN
ijassa-121	119	6	)	)	PUNCT
ijassa-121	119	7	(	(	PUNCT
ijassa-121	119	8	1	1	NUM
ijassa-121	119	9	+	+	NUM
ijassa-121	119	10	γ(ν0	γ(ν0	NOUN
ijassa-121	120	1	−	−	PROPN
ijassa-121	120	2	ν1	ν1	PROPN
ijassa-121	120	3	)	)	PUNCT
ijassa-121	120	4	−1	−1	NOUN
ijassa-121	120	5	−1	−1	NOUN
ijassa-121	120	6	ν0	ν0	PROPN
ijassa-121	120	7	ν1	ν1	NOUN
ijassa-121	120	8	)	)	PUNCT
ijassa-121	120	9	=	=	SYM
ijassa-121	120	10	1	1	NUM
ijassa-121	120	11	mn	mn	PROPN
ijassa-121	120	12	(	(	PUNCT
ijassa-121	120	13	1	1	NUM
ijassa-121	120	14	λ0(1−ω	λ0(1−ω	NOUN
ijassa-121	120	15	)	)	PUNCT
ijassa-121	121	1	+	+	CCONJ
ijassa-121	121	2	γ	γ	X
ijassa-121	121	3	−	−	PROPN
ijassa-121	121	4	1	1	NUM
ijassa-121	121	5	λ0(1−ω	λ0(1−ω	NOUN
ijassa-121	121	6	)	)	PUNCT
ijassa-121	121	7	−	−	PROPN
ijassa-121	121	8	1	1	NUM
ijassa-121	121	9	λ0(1−ω	λ0(1−ω	NOUN
ijassa-121	121	10	)	)	PUNCT
ijassa-121	121	11	1	1	NUM
ijassa-121	121	12	λ0(1−ω	λ0(1−ω	NOUN
ijassa-121	121	13	)	)	PUNCT
ijassa-121	122	1	+	+	CCONJ
ijassa-121	122	2	1	1	NUM
ijassa-121	122	3	λ1(ω	λ1(ω	NOUN
ijassa-121	122	4	)	)	PUNCT
ijassa-121	122	5	)	)	PUNCT
ijassa-121	123	1	the	the	DET
ijassa-121	123	2	variance	variance	NOUN
ijassa-121	123	3	of	of	ADP
ijassa-121	123	4	the	the	DET
ijassa-121	123	5	estimate	estimate	NOUN
ijassa-121	123	6	for	for	ADP
ijassa-121	123	7	β	β	X
ijassa-121	123	8	is	be	AUX
ijassa-121	123	9	given	give	VERB
ijassa-121	123	10	by	by	ADP
ijassa-121	123	11	cov	cov	PROPN
ijassa-121	123	12	(	(	PUNCT
ijassa-121	123	13	β̂	β̂	ADP
ijassa-121	123	14	)	)	PUNCT
ijassa-121	123	15	=	=	NOUN
ijassa-121	124	1	[	[	X
ijassa-121	124	2	m−1(ω)]22	m−1(ω)]22	X
ijassa-121	124	3	=	=	SYM
ijassa-121	124	4	1	1	NUM
ijassa-121	124	5	mn	mn	NOUN
ijassa-121	124	6	[	[	PUNCT
ijassa-121	124	7	1	1	NUM
ijassa-121	124	8	λ0(1−	λ0(1−	NOUN
ijassa-121	124	9	ω	ω	NUM
ijassa-121	124	10	)	)	PUNCT
ijassa-121	125	1	+	+	CCONJ
ijassa-121	125	2	1	1	NUM
ijassa-121	125	3	λ1ω	λ1ω	NOUN
ijassa-121	125	4	]	]	PUNCT
ijassa-121	125	5	.	.	PUNCT
ijassa-121	126	1	the	the	DET
ijassa-121	126	2	variance	variance	NOUN
ijassa-121	126	3	of	of	ADP
ijassa-121	126	4	β̂	β̂	ADP
ijassa-121	126	5	is	be	AUX
ijassa-121	126	6	minimized	minimize	VERB
ijassa-121	126	7	at	at	ADP
ijassa-121	126	8	ω	ω	PROPN
ijassa-121	126	9	=	=	NOUN
ijassa-121	126	10	√	√	PROPN
ijassa-121	126	11	λ0/	λ0/	NOUN
ijassa-121	126	12	(	(	PUNCT
ijassa-121	126	13	√	√	NOUN
ijassa-121	126	14	λ0	λ0	NOUN
ijassa-121	126	15	+	+	NOUN
ijassa-121	126	16	√	√	NUM
ijassa-121	126	17	λ1	λ1	NUM
ijassa-121	126	18	)	)	PUNCT
ijassa-121	126	19	.	.	PUNCT
ijassa-121	127	1	therefore	therefore	ADV
ijassa-121	127	2	we	we	PRON
ijassa-121	127	3	have	have	AUX
ijassa-121	127	4	theorem	theorem	VERB
ijassa-121	127	5	2	2	NUM
ijassa-121	127	6	for	for	ADP
ijassa-121	127	7	the	the	DET
ijassa-121	127	8	model	model	NOUN
ijassa-121	127	9	(	(	PUNCT
ijassa-121	127	10	2	2	NUM
ijassa-121	127	11	)	)	PUNCT
ijassa-121	127	12	with	with	ADP
ijassa-121	127	13	mi	mi	PROPN
ijassa-121	128	1	=	=	VERB
ijassa-121	128	2	m	m	PROPN
ijassa-121	128	3	(	(	PUNCT
ijassa-121	128	4	i	i	NOUN
ijassa-121	128	5	=	=	NOUN
ijassa-121	128	6	1	1	NUM
ijassa-121	128	7	,	,	PUNCT
ijassa-121	128	8	.	.	PUNCT
ijassa-121	128	9	.	.	PUNCT
ijassa-121	128	10	.	.	PUNCT
ijassa-121	128	11	,	,	PUNCT
ijassa-121	128	12	n	n	CCONJ
ijassa-121	128	13	)	)	PUNCT
ijassa-121	128	14	,	,	PUNCT
ijassa-121	128	15	and	and	CCONJ
ijassa-121	128	16	λ(x	λ(x	ADJ
ijassa-121	128	17	)	)	PUNCT
ijassa-121	128	18	satisfying	satisfy	VERB
ijassa-121	128	19	(	(	PUNCT
ijassa-121	128	20	6	6	NUM
ijassa-121	128	21	)	)	PUNCT
ijassa-121	128	22	the	the	DET
ijassa-121	128	23	ds	ds	ADJ
ijassa-121	128	24	-	-	PUNCT
ijassa-121	128	25	optimal	optimal	ADJ
ijassa-121	128	26	design	design	NOUN
ijassa-121	128	27	is	be	AUX
ijassa-121	128	28	ξ∗ds	ξ∗ds	NOUN
ijassa-121	128	29	=	=	SYM
ijassa-121	128	30	(	(	PUNCT
ijassa-121	128	31	0	0	NUM
ijassa-121	128	32	,	,	PUNCT
ijassa-121	128	33	1	1	NUM
ijassa-121	128	34	1−	1−	NUM
ijassa-121	128	35	ωds	ωds	NOUN
ijassa-121	128	36	,	,	PUNCT
ijassa-121	128	37	ωds	ωds	ADJ
ijassa-121	128	38	)	)	PUNCT
ijassa-121	128	39	,	,	PUNCT
ijassa-121	128	40	ωds	ωds	ADJ
ijassa-121	128	41	=	=	NOUN
ijassa-121	129	1	√	√	NUM
ijassa-121	129	2	λ0√	λ0√	NOUN
ijassa-121	129	3	λ0	λ0	NOUN
ijassa-121	129	4	+	+	CCONJ
ijassa-121	129	5	√	√	NOUN
ijassa-121	129	6	λ1	λ1	PROPN
ijassa-121	129	7	.	.	PUNCT
ijassa-121	130	1	advances	advance	NOUN
ijassa-121	130	2	in	in	ADP
ijassa-121	130	3	systems	system	NOUN
ijassa-121	130	4	science	science	NOUN
ijassa-121	130	5	and	and	CCONJ
ijassa-121	130	6	applications	application	NOUN
ijassa-121	130	7	(	(	PUNCT
ijassa-121	130	8	2012	2012	NUM
ijassa-121	130	9	)	)	PUNCT
ijassa-121	130	10	vol.12	vol.12	NOUN
ijassa-121	130	11	no.4	no.4	PROPN
ijassa-121	130	12	393	393	NUM
ijassa-121	130	13	consider	consider	VERB
ijassa-121	130	14	the	the	DET
ijassa-121	130	15	g	g	NOUN
ijassa-121	130	16	-	-	PUNCT
ijassa-121	130	17	optimality	optimality	NOUN
ijassa-121	130	18	,	,	PUNCT
ijassa-121	130	19	then	then	ADV
ijassa-121	130	20	φ(m(ω	φ(m(ω	PROPN
ijassa-121	130	21	)	)	PUNCT
ijassa-121	130	22	)	)	PUNCT
ijassa-121	131	1	=	=	SYM
ijassa-121	131	2	max	max	PROPN
ijassa-121	131	3	x∈[0,1	x∈[0,1	SYM
ijassa-121	131	4	]	]	X
ijassa-121	131	5	d(ω	d(ω	PROPN
ijassa-121	131	6	,	,	PUNCT
ijassa-121	131	7	x	x	NOUN
ijassa-121	131	8	)	)	PUNCT
ijassa-121	131	9	.	.	PUNCT
ijassa-121	132	1	here	here	ADV
ijassa-121	132	2	d(ω	d(ω	PROPN
ijassa-121	132	3	,	,	PUNCT
ijassa-121	132	4	x	x	PRON
ijassa-121	132	5	)	)	PUNCT
ijassa-121	132	6	is	be	AUX
ijassa-121	132	7	the	the	DET
ijassa-121	132	8	variance	variance	NOUN
ijassa-121	132	9	of	of	ADP
ijassa-121	132	10	the	the	DET
ijassa-121	132	11	predicted	predict	VERB
ijassa-121	132	12	value	value	NOUN
ijassa-121	132	13	of	of	ADP
ijassa-121	132	14	the	the	DET
ijassa-121	132	15	response	response	NOUN
ijassa-121	132	16	,	,	PUNCT
ijassa-121	132	17	which	which	PRON
ijassa-121	132	18	is	be	AUX
ijassa-121	132	19	given	give	VERB
ijassa-121	132	20	by	by	ADP
ijassa-121	132	21	d(x	d(x	PROPN
ijassa-121	132	22	,	,	PUNCT
ijassa-121	132	23	ω	ω	NOUN
ijassa-121	132	24	)	)	PUNCT
ijassa-121	132	25	=	=	PUNCT
ijassa-121	132	26	(	(	PUNCT
ijassa-121	132	27	1	1	NUM
ijassa-121	132	28	x	x	SYM
ijassa-121	132	29	)	)	PUNCT
ijassa-121	132	30	m−1(ω	m−1(ω	PROPN
ijassa-121	132	31	)	)	PUNCT
ijassa-121	132	32	(	(	PUNCT
ijassa-121	132	33	1	1	NUM
ijassa-121	132	34	x	x	X
ijassa-121	132	35	)	)	PUNCT
ijassa-121	132	36	=	=	SYM
ijassa-121	132	37	1	1	NUM
ijassa-121	132	38	mn	mn	PROPN
ijassa-121	132	39	{	{	PUNCT
ijassa-121	132	40	[	[	PUNCT
ijassa-121	132	41	1	1	NUM
ijassa-121	132	42	λ0(1−	λ0(1−	NOUN
ijassa-121	132	43	ω	ω	NUM
ijassa-121	132	44	)	)	PUNCT
ijassa-121	133	1	+	+	CCONJ
ijassa-121	133	2	1	1	NUM
ijassa-121	133	3	λ1ω	λ1ω	NOUN
ijassa-121	133	4	]	]	PUNCT
ijassa-121	134	1	x2	x2	INTJ
ijassa-121	134	2	−	−	NOUN
ijassa-121	135	1	2x	2x	NUM
ijassa-121	135	2	λ0(1−	λ0(1−	X
ijassa-121	135	3	ω	ω	NUM
ijassa-121	135	4	)	)	PUNCT
ijassa-121	135	5	+	+	CCONJ
ijassa-121	135	6	1	1	NUM
ijassa-121	135	7	λ0(1−	λ0(1−	NOUN
ijassa-121	135	8	ω	ω	NUM
ijassa-121	135	9	)	)	PUNCT
ijassa-121	135	10	+	+	CCONJ
ijassa-121	135	11	γ	γ	X
ijassa-121	135	12	}	}	PUNCT
ijassa-121	135	13	.	.	PUNCT
ijassa-121	136	1	as	as	ADP
ijassa-121	136	2	d(ω	d(ω	PROPN
ijassa-121	136	3	,	,	PUNCT
ijassa-121	136	4	x	x	PRON
ijassa-121	136	5	)	)	PUNCT
ijassa-121	136	6	is	be	AUX
ijassa-121	136	7	a	a	DET
ijassa-121	136	8	polynomial	polynomial	NOUN
ijassa-121	136	9	of	of	ADP
ijassa-121	136	10	degree	degree	NOUN
ijassa-121	136	11	2	2	NUM
ijassa-121	136	12	with	with	ADP
ijassa-121	136	13	positive	positive	ADJ
ijassa-121	136	14	leading	leading	ADJ
ijassa-121	136	15	term	term	NOUN
ijassa-121	136	16	,	,	PUNCT
ijassa-121	136	17	its	its	PRON
ijassa-121	136	18	maximum	maximum	NOUN
ijassa-121	136	19	is	be	AUX
ijassa-121	136	20	attained	attain	VERB
ijassa-121	136	21	either	either	CCONJ
ijassa-121	136	22	x	x	PUNCT
ijassa-121	136	23	=	=	SYM
ijassa-121	136	24	0	0	NUM
ijassa-121	136	25	or	or	CCONJ
ijassa-121	136	26	x	x	SYM
ijassa-121	137	1	=	=	SYM
ijassa-121	137	2	1	1	NUM
ijassa-121	137	3	or	or	CCONJ
ijassa-121	137	4	both	both	PRON
ijassa-121	137	5	,	,	PUNCT
ijassa-121	137	6	i.e.	i.e.	X
ijassa-121	137	7	,max	,max	PUNCT
ijassa-121	137	8	d(ω	d(ω	PROPN
ijassa-121	137	9	,	,	PUNCT
ijassa-121	137	10	x	x	NOUN
ijassa-121	137	11	)	)	PUNCT
ijassa-121	137	12	=	=	SYM
ijassa-121	137	13	max	max	PROPN
ijassa-121	137	14	x∈[0,1	x∈[0,1	PROPN
ijassa-121	137	15	]	]	X
ijassa-121	137	16	{	{	PUNCT
ijassa-121	137	17	d(0	d(0	NOUN
ijassa-121	137	18	,	,	PUNCT
ijassa-121	137	19	ω	ω	NOUN
ijassa-121	137	20	)	)	PUNCT
ijassa-121	137	21	,	,	PUNCT
ijassa-121	137	22	d(1	d(1	PROPN
ijassa-121	137	23	,	,	PUNCT
ijassa-121	137	24	ω	ω	NOUN
ijassa-121	137	25	)	)	PUNCT
ijassa-121	137	26	}	}	PUNCT
ijassa-121	137	27	.	.	PUNCT
ijassa-121	138	1	note	note	VERB
ijassa-121	138	2	that	that	SCONJ
ijassa-121	138	3	d(0	d(0	NOUN
ijassa-121	138	4	,	,	PUNCT
ijassa-121	138	5	ω	ω	NOUN
ijassa-121	138	6	)	)	PUNCT
ijassa-121	138	7	=	=	SYM
ijassa-121	138	8	1	1	NUM
ijassa-121	138	9	mn	mn	PROPN
ijassa-121	138	10	{	{	PUNCT
ijassa-121	138	11	1	1	NUM
ijassa-121	138	12	λ0(1−	λ0(1−	NOUN
ijassa-121	138	13	ω	ω	NUM
ijassa-121	138	14	)	)	PUNCT
ijassa-121	139	1	+	+	CCONJ
ijassa-121	139	2	γ	γ	NOUN
ijassa-121	139	3	}	}	PUNCT
ijassa-121	139	4	is	be	AUX
ijassa-121	139	5	strictly	strictly	ADV
ijassa-121	139	6	increasing	increase	VERB
ijassa-121	139	7	in	in	ADP
ijassa-121	139	8	ω	ω	NOUN
ijassa-121	139	9	,	,	PUNCT
ijassa-121	139	10	d(1	d(1	PROPN
ijassa-121	139	11	,	,	PUNCT
ijassa-121	139	12	ω	ω	NOUN
ijassa-121	139	13	)	)	PUNCT
ijassa-121	139	14	=	=	SYM
ijassa-121	139	15	1	1	NUM
ijassa-121	139	16	mn	mn	PROPN
ijassa-121	139	17	{	{	PUNCT
ijassa-121	139	18	1	1	NUM
ijassa-121	139	19	λ1(ω	λ1(ω	NOUN
ijassa-121	139	20	)	)	PUNCT
ijassa-121	139	21	+	+	CCONJ
ijassa-121	139	22	γ	γ	NOUN
ijassa-121	139	23	}	}	PUNCT
ijassa-121	139	24	is	be	AUX
ijassa-121	139	25	strictly	strictly	ADV
ijassa-121	139	26	decreasing	decrease	VERB
ijassa-121	139	27	in	in	ADP
ijassa-121	139	28	ω	ω	PROPN
ijassa-121	139	29	.	.	PUNCT
ijassa-121	140	1	thus	thus	ADV
ijassa-121	140	2	min	min	PROPN
ijassa-121	140	3	ω∈[0,1	ω∈[0,1	ADV
ijassa-121	140	4	]	]	X
ijassa-121	140	5	max	max	PROPN
ijassa-121	140	6	x∈[0,1	x∈[0,1	PROPN
ijassa-121	140	7	]	]	X
ijassa-121	140	8	d(x	d(x	PROPN
ijassa-121	140	9	,	,	PUNCT
ijassa-121	140	10	ω	ω	NOUN
ijassa-121	140	11	)	)	PUNCT
ijassa-121	140	12	is	be	AUX
ijassa-121	140	13	attained	attain	VERB
ijassa-121	140	14	when	when	SCONJ
ijassa-121	140	15	d(0	d(0	PROPN
ijassa-121	140	16	,	,	PUNCT
ijassa-121	140	17	ω	ω	NOUN
ijassa-121	140	18	)	)	PUNCT
ijassa-121	140	19	=	=	SYM
ijassa-121	140	20	d(1	d(1	PROPN
ijassa-121	140	21	,	,	PUNCT
ijassa-121	140	22	ω	ω	NOUN
ijassa-121	140	23	)	)	PUNCT
ijassa-121	140	24	,	,	PUNCT
ijassa-121	140	25	i.e.	i.e.	X
ijassa-121	140	26	,	,	PUNCT
ijassa-121	140	27	1	1	NUM
ijassa-121	140	28	λ0(1−	λ0(1−	NOUN
ijassa-121	140	29	ω	ω	NUM
ijassa-121	140	30	)	)	PUNCT
ijassa-121	140	31	=	=	NOUN
ijassa-121	140	32	1	1	NUM
ijassa-121	140	33	λ1(ω	λ1(ω	NOUN
ijassa-121	140	34	)	)	PUNCT
ijassa-121	140	35	.	.	PUNCT
ijassa-121	141	1	so	so	ADV
ijassa-121	141	2	we	we	PRON
ijassa-121	141	3	have	have	AUX
ijassa-121	141	4	theorem	theorem	VERB
ijassa-121	141	5	3	3	NUM
ijassa-121	141	6	for	for	ADP
ijassa-121	141	7	the	the	DET
ijassa-121	141	8	model	model	NOUN
ijassa-121	141	9	(	(	PUNCT
ijassa-121	141	10	2	2	NUM
ijassa-121	141	11	)	)	PUNCT
ijassa-121	141	12	with	with	ADP
ijassa-121	141	13	mi	mi	PROPN
ijassa-121	142	1	=	=	VERB
ijassa-121	142	2	m	m	PROPN
ijassa-121	142	3	(	(	PUNCT
ijassa-121	142	4	i	i	NOUN
ijassa-121	142	5	=	=	NOUN
ijassa-121	142	6	1	1	NUM
ijassa-121	142	7	,	,	PUNCT
ijassa-121	142	8	.	.	PUNCT
ijassa-121	142	9	.	.	PUNCT
ijassa-121	142	10	.	.	PUNCT
ijassa-121	142	11	,	,	PUNCT
ijassa-121	142	12	n	n	CCONJ
ijassa-121	142	13	)	)	PUNCT
ijassa-121	142	14	,	,	PUNCT
ijassa-121	142	15	and	and	CCONJ
ijassa-121	142	16	λ(x	λ(x	ADJ
ijassa-121	142	17	)	)	PUNCT
ijassa-121	142	18	satisfying	satisfy	VERB
ijassa-121	142	19	(	(	PUNCT
ijassa-121	142	20	6	6	NUM
ijassa-121	142	21	)	)	PUNCT
ijassa-121	142	22	the	the	DET
ijassa-121	142	23	g	g	NOUN
ijassa-121	142	24	-	-	PUNCT
ijassa-121	142	25	optimal	optimal	ADJ
ijassa-121	142	26	design	design	NOUN
ijassa-121	142	27	is	be	AUX
ijassa-121	142	28	ξg	ξg	NOUN
ijassa-121	142	29	∗	∗	NOUN
ijassa-121	142	30	=	=	PUNCT
ijassa-121	142	31	(	(	PUNCT
ijassa-121	142	32	0	0	NUM
ijassa-121	142	33	,	,	PUNCT
ijassa-121	142	34	1	1	NUM
ijassa-121	142	35	1−	1−	NUM
ijassa-121	142	36	ωg	ωg	SYM
ijassa-121	142	37	,	,	PUNCT
ijassa-121	142	38	ωg	ωg	NOUN
ijassa-121	142	39	)	)	PUNCT
ijassa-121	142	40	,	,	PUNCT
ijassa-121	142	41	ωg	ωg	NOUN
ijassa-121	142	42	=	=	NOUN
ijassa-121	142	43	λ0	λ0	NOUN
ijassa-121	142	44	λ0	λ0	NOUN
ijassa-121	142	45	+	+	X
ijassa-121	142	46	λ1	λ1	ADJ
ijassa-121	142	47	.	.	PUNCT
ijassa-121	143	1	for	for	ADP
ijassa-121	143	2	the	the	DET
ijassa-121	143	3	i	i	NOUN
ijassa-121	143	4	-	-	PUNCT
ijassa-121	143	5	optimality	optimality	NOUN
ijassa-121	143	6	,	,	PUNCT
ijassa-121	143	7	φ(m(ω	φ(m(ω	NOUN
ijassa-121	143	8	)	)	PUNCT
ijassa-121	143	9	)	)	PUNCT
ijassa-121	144	1	=	=	PUNCT
ijassa-121	144	2	∫	∫	PROPN
ijassa-121	145	1	1	1	NUM
ijassa-121	145	2	0	0	NUM
ijassa-121	145	3	d(x	d(x	NOUN
ijassa-121	145	4	,	,	PUNCT
ijassa-121	145	5	ω)dx	ω)dx	PROPN
ijassa-121	145	6	=	=	SYM
ijassa-121	145	7	1	1	NUM
ijassa-121	145	8	mn	mn	PROPN
ijassa-121	145	9	[	[	PUNCT
ijassa-121	145	10	γ	γ	X
ijassa-121	145	11	+	+	PROPN
ijassa-121	145	12	1	1	NUM
ijassa-121	145	13	3λ0(1−	3λ0(1−	NUM
ijassa-121	145	14	ω	ω	NUM
ijassa-121	145	15	)	)	PUNCT
ijassa-121	145	16	+	+	CCONJ
ijassa-121	145	17	1	1	NUM
ijassa-121	145	18	3λ1ω	3λ1ω	NOUN
ijassa-121	145	19	]	]	PUNCT
ijassa-121	145	20	.	.	PUNCT
ijassa-121	146	1	which	which	PRON
ijassa-121	146	2	is	be	AUX
ijassa-121	146	3	minimized	minimize	VERB
ijassa-121	146	4	at	at	ADP
ijassa-121	146	5	ω	ω	PROPN
ijassa-121	146	6	=	=	NOUN
ijassa-121	146	7	√	√	PROPN
ijassa-121	146	8	λ0/	λ0/	NOUN
ijassa-121	146	9	(	(	PUNCT
ijassa-121	146	10	√	√	NOUN
ijassa-121	146	11	λ0	λ0	NOUN
ijassa-121	146	12	+	+	NOUN
ijassa-121	146	13	√	√	NUM
ijassa-121	146	14	λ1	λ1	NUM
ijassa-121	146	15	)	)	PUNCT
ijassa-121	147	1	=	=	PRON
ijassa-121	147	2	ωds	ωds	NOUN
ijassa-121	147	3	.	.	PUNCT
ijassa-121	148	1	so	so	ADV
ijassa-121	148	2	we	we	PRON
ijassa-121	148	3	have	have	AUX
ijassa-121	148	4	theorem	theorem	VERB
ijassa-121	148	5	4	4	NUM
ijassa-121	148	6	for	for	ADP
ijassa-121	148	7	the	the	DET
ijassa-121	148	8	model	model	NOUN
ijassa-121	148	9	(	(	PUNCT
ijassa-121	148	10	2	2	NUM
ijassa-121	148	11	)	)	PUNCT
ijassa-121	148	12	with	with	ADP
ijassa-121	148	13	mi	mi	PROPN
ijassa-121	149	1	=	=	VERB
ijassa-121	149	2	m	m	PROPN
ijassa-121	149	3	(	(	PUNCT
ijassa-121	149	4	i	i	NOUN
ijassa-121	149	5	=	=	NOUN
ijassa-121	149	6	1	1	NUM
ijassa-121	149	7	,	,	PUNCT
ijassa-121	149	8	.	.	PUNCT
ijassa-121	149	9	.	.	PUNCT
ijassa-121	149	10	.	.	PUNCT
ijassa-121	149	11	,	,	PUNCT
ijassa-121	149	12	n	n	CCONJ
ijassa-121	149	13	)	)	PUNCT
ijassa-121	149	14	,	,	PUNCT
ijassa-121	149	15	and	and	CCONJ
ijassa-121	149	16	λ(x	λ(x	ADJ
ijassa-121	149	17	)	)	PUNCT
ijassa-121	149	18	satisfying	satisfy	VERB
ijassa-121	149	19	(	(	PUNCT
ijassa-121	149	20	6	6	NUM
ijassa-121	149	21	)	)	PUNCT
ijassa-121	149	22	the	the	DET
ijassa-121	149	23	i	i	NOUN
ijassa-121	149	24	-	-	PUNCT
ijassa-121	149	25	optimal	optimal	ADJ
ijassa-121	149	26	design	design	NOUN
ijassa-121	149	27	is	be	AUX
ijassa-121	149	28	ξ∗i	ξ∗i	NUM
ijassa-121	149	29	=	=	SYM
ijassa-121	149	30	(	(	PUNCT
ijassa-121	149	31	0	0	NUM
ijassa-121	149	32	,	,	PUNCT
ijassa-121	149	33	1	1	NUM
ijassa-121	149	34	1−	1−	NUM
ijassa-121	149	35	ωi	ωi	NUM
ijassa-121	149	36	,	,	PUNCT
ijassa-121	149	37	ωi	ωi	PROPN
ijassa-121	149	38	)	)	PUNCT
ijassa-121	149	39	,	,	PUNCT
ijassa-121	149	40	ωi	ωi	X
ijassa-121	149	41	=	=	SYM
ijassa-121	149	42	√	√	NUM
ijassa-121	149	43	λ0√	λ0√	NOUN
ijassa-121	149	44	λ0	λ0	NOUN
ijassa-121	149	45	+	+	CCONJ
ijassa-121	149	46	√	√	NOUN
ijassa-121	149	47	λ1	λ1	PROPN
ijassa-121	149	48	.	.	PUNCT
ijassa-121	150	1	394	394	NUM
ijassa-121	150	2	jing	jing	ADJ
ijassa-121	150	3	cheng	cheng	PROPN
ijassa-121	150	4	:	:	PUNCT
ijassa-121	150	5	optimal	optimal	ADJ
ijassa-121	150	6	designs	design	NOUN
ijassa-121	150	7	in	in	ADP
ijassa-121	150	8	random	random	ADJ
ijassa-121	150	9	intercept	intercept	NOUN
ijassa-121	150	10	model	model	NOUN
ijassa-121	150	11	with	with	ADP
ijassa-121	150	12	heteroscedastic	heteroscedastic	ADJ
ijassa-121	150	13	errors	error	NOUN
ijassa-121	150	14	for	for	ADP
ijassa-121	150	15	the	the	DET
ijassa-121	150	16	a	a	DET
ijassa-121	150	17	-	-	PUNCT
ijassa-121	150	18	optimality	optimality	NOUN
ijassa-121	150	19	φ(m(ω	φ(m(ω	NOUN
ijassa-121	150	20	)	)	PUNCT
ijassa-121	150	21	)	)	PUNCT
ijassa-121	150	22	=	=	PUNCT
ijassa-121	150	23	tr	tr	PROPN
ijassa-121	150	24	(	(	PUNCT
ijassa-121	150	25	m−1(ω	m−1(ω	PROPN
ijassa-121	150	26	)	)	PUNCT
ijassa-121	150	27	)	)	PUNCT
ijassa-121	150	28	=	=	SYM
ijassa-121	150	29	1	1	NUM
ijassa-121	150	30	mn	mn	PROPN
ijassa-121	150	31	[	[	PUNCT
ijassa-121	150	32	1	1	NUM
ijassa-121	150	33	λ1ω	λ1ω	NOUN
ijassa-121	150	34	+	+	NUM
ijassa-121	150	35	2	2	NUM
ijassa-121	150	36	λ0(1−	λ0(1−	NOUN
ijassa-121	150	37	ω	ω	NUM
ijassa-121	150	38	)	)	PUNCT
ijassa-121	150	39	+	+	CCONJ
ijassa-121	150	40	γ	γ	X
ijassa-121	150	41	]	]	PUNCT
ijassa-121	150	42	.	.	PUNCT
ijassa-121	151	1	it	it	PRON
ijassa-121	151	2	is	be	AUX
ijassa-121	151	3	easy	easy	ADJ
ijassa-121	151	4	to	to	PART
ijassa-121	151	5	verify	verify	VERB
ijassa-121	151	6	that	that	SCONJ
ijassa-121	151	7	tr	tr	PUNCT
ijassa-121	151	8	(	(	PUNCT
ijassa-121	151	9	m−1(ω	m−1(ω	PROPN
ijassa-121	151	10	)	)	PUNCT
ijassa-121	151	11	)	)	PUNCT
ijassa-121	151	12	is	be	AUX
ijassa-121	151	13	minimized	minimize	VERB
ijassa-121	151	14	at	at	ADP
ijassa-121	151	15	ω	ω	PROPN
ijassa-121	151	16	=	=	SYM
ijassa-121	152	1	√	√	ADP
ijassa-121	152	2	λ0√	λ0√	PROPN
ijassa-121	152	3	λ0	λ0	NOUN
ijassa-121	152	4	+	+	CCONJ
ijassa-121	152	5	√	√	PROPN
ijassa-121	152	6	2λ1	2λ1	NUM
ijassa-121	152	7	.therefore	.therefore	NOUN
ijassa-121	152	8	we	we	PRON
ijassa-121	152	9	have	have	AUX
ijassa-121	152	10	theorem	theorem	VERB
ijassa-121	152	11	5	5	NUM
ijassa-121	152	12	for	for	ADP
ijassa-121	152	13	the	the	DET
ijassa-121	152	14	model	model	NOUN
ijassa-121	152	15	(	(	PUNCT
ijassa-121	152	16	2	2	NUM
ijassa-121	152	17	)	)	PUNCT
ijassa-121	152	18	with	with	ADP
ijassa-121	152	19	mi	mi	PROPN
ijassa-121	152	20	=	=	VERB
ijassa-121	152	21	m	m	PROPN
ijassa-121	152	22	(	(	PUNCT
ijassa-121	152	23	i	i	NOUN
ijassa-121	152	24	=	=	NOUN
ijassa-121	152	25	1	1	NUM
ijassa-121	152	26	,	,	PUNCT
ijassa-121	152	27	.	.	PUNCT
ijassa-121	152	28	.	.	PUNCT
ijassa-121	152	29	.	.	PUNCT
ijassa-121	152	30	,	,	PUNCT
ijassa-121	152	31	n	n	CCONJ
ijassa-121	152	32	)	)	PUNCT
ijassa-121	152	33	,	,	PUNCT
ijassa-121	152	34	and	and	CCONJ
ijassa-121	152	35	λ(x	λ(x	ADJ
ijassa-121	152	36	)	)	PUNCT
ijassa-121	152	37	satisfying	satisfy	VERB
ijassa-121	152	38	(	(	PUNCT
ijassa-121	152	39	6	6	NUM
ijassa-121	152	40	)	)	PUNCT
ijassa-121	152	41	the	the	DET
ijassa-121	152	42	a	a	ADV
ijassa-121	152	43	-	-	PUNCT
ijassa-121	152	44	optimal	optimal	ADJ
ijassa-121	152	45	design	design	NOUN
ijassa-121	152	46	is	be	AUX
ijassa-121	152	47	ξa	ξa	ADP
ijassa-121	152	48	∗	∗	NOUN
ijassa-121	152	49	=	=	PUNCT
ijassa-121	152	50	(	(	PUNCT
ijassa-121	152	51	0	0	NUM
ijassa-121	152	52	,	,	PUNCT
ijassa-121	152	53	1	1	NUM
ijassa-121	152	54	1−	1−	NUM
ijassa-121	152	55	ωa	ωa	ADJ
ijassa-121	152	56	,	,	PUNCT
ijassa-121	152	57	ωa	ωa	ADJ
ijassa-121	152	58	)	)	PUNCT
ijassa-121	152	59	,	,	PUNCT
ijassa-121	152	60	ωi	ωi	X
ijassa-121	152	61	=	=	SYM
ijassa-121	152	62	√	√	NUM
ijassa-121	152	63	λ0√	λ0√	NOUN
ijassa-121	152	64	λ0	λ0	NOUN
ijassa-121	152	65	+	+	CCONJ
ijassa-121	152	66	√	√	NOUN
ijassa-121	152	67	2λ1	2λ1	NUM
ijassa-121	152	68	.	.	PUNCT
ijassa-121	153	1	from	from	ADP
ijassa-121	153	2	above	above	ADP
ijassa-121	153	3	discussion	discussion	NOUN
ijassa-121	153	4	,	,	PUNCT
ijassa-121	153	5	we	we	PRON
ijassa-121	153	6	observe	observe	VERB
ijassa-121	153	7	that	that	SCONJ
ijassa-121	153	8	the	the	DET
ijassa-121	153	9	g-	g-	X
ijassa-121	153	10	,	,	PUNCT
ijassa-121	153	11	ds-	ds-	PROPN
ijassa-121	153	12	,	,	PUNCT
ijassa-121	153	13	iand	iand	VERB
ijassa-121	153	14	a	a	DET
ijassa-121	153	15	-	-	PUNCT
ijassa-121	153	16	optimal	optimal	ADJ
ijassa-121	153	17	designs	design	NOUN
ijassa-121	153	18	only	only	ADV
ijassa-121	153	19	depend	depend	VERB
ijassa-121	153	20	on	on	ADP
ijassa-121	153	21	the	the	DET
ijassa-121	153	22	variances	variance	NOUN
ijassa-121	153	23	at	at	ADP
ijassa-121	153	24	extreme	extreme	ADJ
ijassa-121	153	25	settings	setting	NOUN
ijassa-121	153	26	;	;	PUNCT
ijassa-121	153	27	the	the	DET
ijassa-121	153	28	d	d	ADJ
ijassa-121	153	29	-	-	ADJ
ijassa-121	153	30	optimal	optimal	ADJ
ijassa-121	153	31	design	design	NOUN
ijassa-121	153	32	depends	depend	VERB
ijassa-121	153	33	repeated	repeat	VERB
ijassa-121	153	34	times	time	NOUN
ijassa-121	153	35	m	m	PART
ijassa-121	153	36	and	and	CCONJ
ijassa-121	153	37	variance	variance	NOUN
ijassa-121	153	38	proportion	proportion	NOUN
ijassa-121	153	39	d	d	NOUN
ijassa-121	153	40	and	and	CCONJ
ijassa-121	153	41	error	error	NOUN
ijassa-121	153	42	variances	variance	NOUN
ijassa-121	153	43	at	at	ADP
ijassa-121	153	44	the	the	DET
ijassa-121	153	45	extreme	extreme	ADJ
ijassa-121	153	46	settings	setting	NOUN
ijassa-121	153	47	.	.	PUNCT
ijassa-121	154	1	note	note	VERB
ijassa-121	154	2	that	that	SCONJ
ijassa-121	154	3	the	the	DET
ijassa-121	154	4	particular	particular	ADJ
ijassa-121	154	5	shape	shape	NOUN
ijassa-121	154	6	of	of	ADP
ijassa-121	154	7	λ(x	λ(x	PROPN
ijassa-121	154	8	)	)	PUNCT
ijassa-121	154	9	is	be	AUX
ijassa-121	154	10	immaterial	immaterial	ADJ
ijassa-121	154	11	for	for	ADP
ijassa-121	154	12	the	the	DET
ijassa-121	154	13	results	result	NOUN
ijassa-121	154	14	,	,	PUNCT
ijassa-121	154	15	but	but	CCONJ
ijassa-121	154	16	only	only	ADV
ijassa-121	154	17	its	its	PRON
ijassa-121	154	18	values	value	NOUN
ijassa-121	154	19	at	at	ADP
ijassa-121	154	20	0	0	NUM
ijassa-121	154	21	and	and	CCONJ
ijassa-121	154	22	1	1	NUM
ijassa-121	154	23	,	,	PUNCT
ijassa-121	154	24	as	as	ADV
ijassa-121	154	25	long	long	ADV
ijassa-121	154	26	as	as	SCONJ
ijassa-121	154	27	condition	condition	NOUN
ijassa-121	154	28	(	(	PUNCT
ijassa-121	154	29	6	6	NUM
ijassa-121	154	30	)	)	PUNCT
ijassa-121	154	31	is	be	AUX
ijassa-121	154	32	satisfied	satisfied	ADJ
ijassa-121	154	33	.	.	PUNCT
ijassa-121	155	1	specially	specially	ADV
ijassa-121	155	2	,	,	PUNCT
ijassa-121	155	3	when	when	SCONJ
ijassa-121	155	4	heteroscedastic	heteroscedastic	ADJ
ijassa-121	155	5	structure	structure	NOUN
ijassa-121	155	6	satisfies	satisfy	VERB
ijassa-121	155	7	λ0	λ0	NOUN
ijassa-121	155	8	=	=	SYM
ijassa-121	155	9	λ1	λ1	PROPN
ijassa-121	155	10	=	=	SYM
ijassa-121	155	11	max	max	PROPN
ijassa-121	155	12	x∈[0,1	x∈[0,1	X
ijassa-121	155	13	]	]	X
ijassa-121	156	1	λ(x	λ(x	X
ijassa-121	156	2	)	)	PUNCT
ijassa-121	156	3	,	,	PUNCT
ijassa-121	156	4	the	the	DET
ijassa-121	156	5	optimal	optimal	ADJ
ijassa-121	156	6	designs	design	NOUN
ijassa-121	156	7	discussed	discuss	VERB
ijassa-121	156	8	above	above	ADV
ijassa-121	156	9	are	be	AUX
ijassa-121	156	10	independent	independent	ADJ
ijassa-121	156	11	of	of	ADP
ijassa-121	156	12	the	the	DET
ijassa-121	156	13	variance	variance	NOUN
ijassa-121	156	14	ratio	ratio	NOUN
ijassa-121	156	15	d.	d.	PROPN
ijassa-121	156	16	these	these	DET
ijassa-121	156	17	optimal	optimal	ADJ
ijassa-121	156	18	designs	design	NOUN
ijassa-121	156	19	are	be	AUX
ijassa-121	156	20	the	the	DET
ijassa-121	156	21	same	same	ADJ
ijassa-121	156	22	as	as	ADP
ijassa-121	156	23	the	the	DET
ijassa-121	156	24	corresponding	corresponding	ADJ
ijassa-121	156	25	optimal	optimal	ADJ
ijassa-121	156	26	designs	design	NOUN
ijassa-121	156	27	in	in	ADP
ijassa-121	156	28	the	the	DET
ijassa-121	156	29	linear	linear	ADJ
ijassa-121	156	30	regression	regression	NOUN
ijassa-121	156	31	model	model	NOUN
ijassa-121	156	32	without	without	ADP
ijassa-121	156	33	any	any	DET
ijassa-121	156	34	random	random	ADJ
ijassa-121	156	35	effects	effect	NOUN
ijassa-121	156	36	,	,	PUNCT
ijassa-121	156	37	i.e.	i.e.	X
ijassa-121	156	38	,	,	PUNCT
ijassa-121	157	1	ωd	ωd	ADP
ijassa-121	157	2	=	=	PUNCT
ijassa-121	157	3	ωg	ωg	NOUN
ijassa-121	157	4	=	=	SYM
ijassa-121	157	5	ωi	ωi	PROPN
ijassa-121	157	6	=	=	PUNCT
ijassa-121	157	7	ωds	ωds	NOUN
ijassa-121	157	8	=	=	NOUN
ijassa-121	157	9	1	1	NUM
ijassa-121	157	10	2	2	NUM
ijassa-121	157	11	,	,	PUNCT
ijassa-121	157	12	ωa	ωa	PROPN
ijassa-121	157	13	=	=	SYM
ijassa-121	157	14	√	√	PROPN
ijassa-121	157	15	2−	2−	NUM
ijassa-121	157	16	1	1	NUM
ijassa-121	157	17	.	.	NOUN
ijassa-121	157	18	4	4	NUM
ijassa-121	157	19	examples	example	NOUN
ijassa-121	157	20	in	in	ADP
ijassa-121	157	21	this	this	DET
ijassa-121	157	22	section	section	NOUN
ijassa-121	157	23	,	,	PUNCT
ijassa-121	157	24	we	we	PRON
ijassa-121	157	25	consider	consider	VERB
ijassa-121	157	26	three	three	NUM
ijassa-121	157	27	random	random	ADJ
ijassa-121	157	28	intercept	intercept	NOUN
ijassa-121	157	29	models	model	NOUN
ijassa-121	157	30	with	with	ADP
ijassa-121	157	31	the	the	DET
ijassa-121	157	32	following	follow	VERB
ijassa-121	157	33	heteroscedastic	heteroscedastic	ADJ
ijassa-121	157	34	errors	error	NOUN
ijassa-121	157	35	λ(x	λ(x	X
ijassa-121	157	36	)	)	PUNCT
ijassa-121	158	1	=	=	SYM
ijassa-121	159	1	x2	x2	PROPN
ijassa-121	160	1	+	+	CCONJ
ijassa-121	160	2	1	1	NUM
ijassa-121	160	3	,	,	PUNCT
ijassa-121	160	4	λ(x	λ(x	ADJ
ijassa-121	160	5	)	)	PUNCT
ijassa-121	160	6	=	=	SYM
ijassa-121	161	1	1	1	NUM
ijassa-121	161	2	1	1	NUM
ijassa-121	161	3	+	+	CCONJ
ijassa-121	161	4	x	x	SYM
ijassa-121	161	5	,	,	PUNCT
ijassa-121	161	6	λ(x	λ(x	PROPN
ijassa-121	161	7	)	)	PUNCT
ijassa-121	161	8	=	=	PUNCT
ijassa-121	161	9	x4	x4	PROPN
ijassa-121	162	1	+	+	CCONJ
ijassa-121	162	2	1	1	NUM
ijassa-121	162	3	x2	x2	NOUN
ijassa-121	163	1	+	+	NOUN
ijassa-121	163	2	1	1	NUM
ijassa-121	163	3	.	.	PUNCT
ijassa-121	164	1	it	it	PRON
ijassa-121	164	2	is	be	AUX
ijassa-121	164	3	easy	easy	ADJ
ijassa-121	164	4	to	to	PART
ijassa-121	164	5	verify	verify	VERB
ijassa-121	164	6	that	that	SCONJ
ijassa-121	164	7	these	these	DET
ijassa-121	164	8	three	three	NUM
ijassa-121	164	9	λ(x	λ(x	ADJ
ijassa-121	164	10	)	)	PUNCT
ijassa-121	164	11	satisfy	satisfy	VERB
ijassa-121	164	12	the	the	DET
ijassa-121	164	13	condition	condition	NOUN
ijassa-121	164	14	(	(	PUNCT
ijassa-121	164	15	6	6	NUM
ijassa-121	164	16	)	)	PUNCT
ijassa-121	164	17	.	.	PUNCT
ijassa-121	165	1	these	these	DET
ijassa-121	165	2	heteroscedastic	heteroscedastic	ADJ
ijassa-121	165	3	structures	structure	NOUN
ijassa-121	165	4	are	be	AUX
ijassa-121	165	5	also	also	ADV
ijassa-121	165	6	considered	consider	VERB
ijassa-121	165	7	in	in	ADP
ijassa-121	165	8	chang[15	chang[15	NOUN
ijassa-121	165	9	]	]	PUNCT
ijassa-121	165	10	for	for	ADP
ijassa-121	165	11	d	d	ADJ
ijassa-121	165	12	-	-	ADJ
ijassa-121	165	13	optimal	optimal	ADJ
ijassa-121	165	14	designs	design	NOUN
ijassa-121	165	15	in	in	ADP
ijassa-121	165	16	weighted	weight	VERB
ijassa-121	165	17	polynomial	polynomial	ADJ
ijassa-121	165	18	regression	regression	NOUN
ijassa-121	165	19	models	model	NOUN
ijassa-121	165	20	.	.	PUNCT
ijassa-121	166	1	we	we	PRON
ijassa-121	166	2	will	will	AUX
ijassa-121	166	3	give	give	VERB
ijassa-121	166	4	the	the	DET
ijassa-121	166	5	optimal	optimal	ADJ
ijassa-121	166	6	designs	design	NOUN
ijassa-121	166	7	for	for	ADP
ijassa-121	166	8	the	the	DET
ijassa-121	166	9	three	three	NUM
ijassa-121	166	10	models	model	NOUN
ijassa-121	166	11	in	in	ADP
ijassa-121	166	12	terms	term	NOUN
ijassa-121	166	13	for	for	ADP
ijassa-121	166	14	the	the	DET
ijassa-121	166	15	results	result	NOUN
ijassa-121	166	16	given	give	VERB
ijassa-121	166	17	in	in	ADP
ijassa-121	166	18	section	section	NOUN
ijassa-121	166	19	3	3	NUM
ijassa-121	166	20	.	.	PUNCT
ijassa-121	167	1	we	we	PRON
ijassa-121	167	2	also	also	ADV
ijassa-121	167	3	compare	compare	VERB
ijassa-121	167	4	the	the	DET
ijassa-121	167	5	dand	dand	NOUN
ijassa-121	167	6	g	g	NOUN
ijassa-121	167	7	-	-	PUNCT
ijassa-121	167	8	optimal	optimal	ADJ
ijassa-121	167	9	designs	design	NOUN
ijassa-121	167	10	for	for	ADP
ijassa-121	167	11	the	the	DET
ijassa-121	167	12	three	three	NUM
ijassa-121	167	13	models	model	NOUN
ijassa-121	167	14	with	with	ADP
ijassa-121	167	15	the	the	DET
ijassa-121	167	16	equireplicated	equireplicate	VERB
ijassa-121	167	17	design	design	NOUN
ijassa-121	167	18	ω0	ω0	NOUN
ijassa-121	167	19	=	=	SYM
ijassa-121	167	20	0.5	0.5	NUM
ijassa-121	167	21	which	which	PRON
ijassa-121	167	22	is	be	AUX
ijassa-121	167	23	simultaneously	simultaneously	ADV
ijassa-121	167	24	dand	dand	VERB
ijassa-121	167	25	g	g	NOUN
ijassa-121	167	26	-	-	PUNCT
ijassa-121	167	27	optimal	optimal	ADJ
ijassa-121	167	28	for	for	ADP
ijassa-121	167	29	the	the	DET
ijassa-121	167	30	fixed	fix	VERB
ijassa-121	167	31	effects	effect	NOUN
ijassa-121	167	32	only	only	ADV
ijassa-121	167	33	model	model	NOUN
ijassa-121	167	34	(	(	PUNCT
ijassa-121	167	35	d	d	NOUN
ijassa-121	167	36	=	=	PUNCT
ijassa-121	167	37	0	0	NUM
ijassa-121	167	38	)	)	PUNCT
ijassa-121	167	39	in	in	ADP
ijassa-121	167	40	terms	term	NOUN
ijassa-121	167	41	of	of	ADP
ijassa-121	167	42	the	the	DET
ijassa-121	167	43	dand	dand	PROPN
ijassa-121	167	44	g	g	NOUN
ijassa-121	167	45	-	-	PUNCT
ijassa-121	167	46	efficiency	efficiency	NOUN
ijassa-121	167	47	which	which	PRON
ijassa-121	167	48	are	be	AUX
ijassa-121	167	49	defined	define	VERB
ijassa-121	167	50	as	as	ADP
ijassa-121	167	51	following	follow	VERB
ijassa-121	167	52	effd(ω0	effd(ω0	NOUN
ijassa-121	167	53	)	)	PUNCT
ijassa-121	167	54	=	=	PUNCT
ijassa-121	168	1	(	(	PUNCT
ijassa-121	168	2	|m(ω0)|	|m(ω0)|	NOUN
ijassa-121	168	3	|m(ω)|	|m(ω)|	PROPN
ijassa-121	168	4	)	)	PUNCT
ijassa-121	168	5	1	1	NUM
ijassa-121	168	6	2	2	NUM
ijassa-121	168	7	,	,	PUNCT
ijassa-121	168	8	effd(ω0	effd(ω0	NOUN
ijassa-121	168	9	)	)	PUNCT
ijassa-121	169	1	=	=	SYM
ijassa-121	169	2	max	max	PROPN
ijassa-121	169	3	x∈[0,1	x∈[0,1	SYM
ijassa-121	169	4	]	]	X
ijassa-121	169	5	d(x	d(x	PROPN
ijassa-121	169	6	,	,	PUNCT
ijassa-121	169	7	ωg	ωg	NOUN
ijassa-121	169	8	)	)	PUNCT
ijassa-121	169	9	max	max	PROPN
ijassa-121	169	10	x∈[0,1	x∈[0,1	NUM
ijassa-121	169	11	]	]	X
ijassa-121	169	12	d(x	d(x	PROPN
ijassa-121	169	13	,	,	PUNCT
ijassa-121	169	14	ω0	ω0	PROPN
ijassa-121	169	15	)	)	PUNCT
ijassa-121	169	16	(	(	PUNCT
ijassa-121	169	17	8)	8)	NUM
ijassa-121	169	18	advances	advance	NOUN
ijassa-121	169	19	in	in	ADP
ijassa-121	169	20	systems	system	NOUN
ijassa-121	169	21	science	science	NOUN
ijassa-121	169	22	and	and	CCONJ
ijassa-121	169	23	applications	application	NOUN
ijassa-121	169	24	(	(	PUNCT
ijassa-121	169	25	2012	2012	NUM
ijassa-121	169	26	)	)	PUNCT
ijassa-121	169	27	vol.12	vol.12	NOUN
ijassa-121	169	28	no.4	no.4	PROPN
ijassa-121	169	29	395	395	NUM
ijassa-121	169	30	example	example	NOUN
ijassa-121	169	31	1	1	NUM
ijassa-121	169	32	for	for	ADP
ijassa-121	169	33	the	the	DET
ijassa-121	169	34	model	model	NOUN
ijassa-121	169	35	(	(	PUNCT
ijassa-121	169	36	2	2	NUM
ijassa-121	169	37	)	)	PUNCT
ijassa-121	169	38	with	with	ADP
ijassa-121	169	39	mi	mi	PROPN
ijassa-121	169	40	=	=	PROPN
ijassa-121	169	41	m(i	m(i	PROPN
ijassa-121	169	42	=	=	SYM
ijassa-121	169	43	1	1	NUM
ijassa-121	169	44	,	,	PUNCT
ijassa-121	169	45	.	.	PUNCT
ijassa-121	169	46	.	.	PUNCT
ijassa-121	170	1	.	.	PUNCT
ijassa-121	170	2	,	,	PUNCT
ijassa-121	171	1	n	n	CCONJ
ijassa-121	171	2	)	)	PUNCT
ijassa-121	171	3	,	,	PUNCT
ijassa-121	171	4	and	and	CCONJ
ijassa-121	171	5	λ(x	λ(x	X
ijassa-121	171	6	)	)	PUNCT
ijassa-121	172	1	=	=	SYM
ijassa-121	173	1	x2	x2	PROPN
ijassa-121	174	1	+	+	CCONJ
ijassa-121	174	2	1	1	NUM
ijassa-121	174	3	,	,	PUNCT
ijassa-121	174	4	from	from	ADP
ijassa-121	174	5	the	the	DET
ijassa-121	174	6	theorems	theorem	NOUN
ijassa-121	174	7	in	in	ADP
ijassa-121	174	8	section	section	NOUN
ijassa-121	174	9	3	3	NUM
ijassa-121	174	10	,	,	PUNCT
ijassa-121	174	11	we	we	PRON
ijassa-121	174	12	obtain	obtain	VERB
ijassa-121	174	13	the	the	DET
ijassa-121	174	14	d-	d-	X
ijassa-121	174	15	,	,	PUNCT
ijassa-121	174	16	g-	g-	X
ijassa-121	174	17	,	,	PUNCT
ijassa-121	174	18	a-	a-	X
ijassa-121	174	19	,	,	PUNCT
ijassa-121	174	20	iand	iand	VERB
ijassa-121	174	21	ds	ds	ADJ
ijassa-121	174	22	-	-	PUNCT
ijassa-121	174	23	optimal	optimal	ADJ
ijassa-121	174	24	proportions	proportion	NOUN
ijassa-121	174	25	as	as	SCONJ
ijassa-121	174	26	follows	follow	VERB
ijassa-121	174	27	:	:	PUNCT
ijassa-121	174	28	ωd	ωd	ADP
ijassa-121	174	29	=	=	SYM
ijassa-121	175	1	√	√	NOUN
ijassa-121	175	2	1	1	NUM
ijassa-121	176	1	+	+	CCONJ
ijassa-121	176	2	γ√	γ√	SYM
ijassa-121	176	3	1	1	NUM
ijassa-121	176	4	+	+	CCONJ
ijassa-121	176	5	γ	γ	X
ijassa-121	176	6	+	+	NOUN
ijassa-121	176	7	√	√	NUM
ijassa-121	176	8	1	1	NUM
ijassa-121	176	9	+	+	CCONJ
ijassa-121	176	10	2γ	2γ	NOUN
ijassa-121	176	11	,	,	PUNCT
ijassa-121	176	12	ωg	ωg	NOUN
ijassa-121	176	13	=	=	PRON
ijassa-121	176	14	ωa	ωa	PROPN
ijassa-121	176	15	=	=	SYM
ijassa-121	176	16	1	1	NUM
ijassa-121	176	17	3	3	NUM
ijassa-121	176	18	,	,	PUNCT
ijassa-121	176	19	ωi	ωi	PROPN
ijassa-121	176	20	=	=	SYM
ijassa-121	176	21	ωds	ωds	NOUN
ijassa-121	176	22	=	=	PUNCT
ijassa-121	176	23	√	√	PROPN
ijassa-121	176	24	2−	2−	NUM
ijassa-121	176	25	1	1	NUM
ijassa-121	176	26	.	.	PUNCT
ijassa-121	177	1	the	the	DET
ijassa-121	177	2	dand	dand	PROPN
ijassa-121	177	3	g	g	PROPN
ijassa-121	177	4	-	-	PUNCT
ijassa-121	177	5	efficiencies	efficiency	NOUN
ijassa-121	177	6	defined	define	VERB
ijassa-121	177	7	by	by	ADP
ijassa-121	177	8	(	(	PUNCT
ijassa-121	177	9	8)	8)	NUM
ijassa-121	177	10	of	of	ADP
ijassa-121	177	11	the	the	DET
ijassa-121	177	12	equireplicated	equireplicated	ADJ
ijassa-121	177	13	design	design	NOUN
ijassa-121	177	14	ω0	ω0	NOUN
ijassa-121	177	15	=	=	SYM
ijassa-121	177	16	0.5	0.5	NUM
ijassa-121	177	17	are	be	AUX
ijassa-121	177	18	as	as	SCONJ
ijassa-121	177	19	follows	follow	NOUN
ijassa-121	177	20	:	:	PUNCT
ijassa-121	177	21	effd(ω0	effd(ω0	PROPN
ijassa-121	177	22	)	)	PUNCT
ijassa-121	178	1	=	=	SYM
ijassa-121	179	1	√	√	NUM
ijassa-121	179	2	1	1	NUM
ijassa-121	179	3	+	+	NUM
ijassa-121	179	4	2γ	2γ	NOUN
ijassa-121	179	5	+	+	X
ijassa-121	179	6	√	√	NUM
ijassa-121	179	7	1	1	NUM
ijassa-121	179	8	+	+	CCONJ
ijassa-121	179	9	γ√	γ√	ADP
ijassa-121	179	10	4	4	NUM
ijassa-121	179	11	+	+	NUM
ijassa-121	179	12	6γ	6γ	NOUN
ijassa-121	179	13	,	,	PUNCT
ijassa-121	179	14	effd(ω0	effd(ω0	PROPN
ijassa-121	179	15	)	)	PUNCT
ijassa-121	179	16	=	=	SYM
ijassa-121	180	1	γ	γ	X
ijassa-121	180	2	+	+	NUM
ijassa-121	180	3	1.5	1.5	NUM
ijassa-121	180	4	γ	γ	NOUN
ijassa-121	180	5	+	+	ADP
ijassa-121	180	6	2	2	NUM
ijassa-121	180	7	.	.	PUNCT
ijassa-121	181	1	it	it	PRON
ijassa-121	181	2	is	be	AUX
ijassa-121	181	3	clear	clear	ADJ
ijassa-121	181	4	that	that	SCONJ
ijassa-121	181	5	effd(ω0	effd(ω0	NOUN
ijassa-121	181	6	)	)	PUNCT
ijassa-121	181	7	decreases	decrease	VERB
ijassa-121	181	8	strictly	strictly	ADV
ijassa-121	181	9	in	in	ADP
ijassa-121	181	10	γ	γ	NOUN
ijassa-121	181	11	and	and	CCONJ
ijassa-121	181	12	ultimately	ultimately	ADV
ijassa-121	181	13	tends	tend	VERB
ijassa-121	181	14	to	to	PART
ijassa-121	181	15	(	(	PUNCT
ijassa-121	181	16	√	√	NUM
ijassa-121	181	17	2	2	NUM
ijassa-121	182	1	+	+	CCONJ
ijassa-121	182	2	1)/	1)/	NUM
ijassa-121	182	3	√	√	NUM
ijassa-121	182	4	6	6	NUM
ijassa-121	182	5	,	,	PUNCT
ijassa-121	182	6	and	and	CCONJ
ijassa-121	182	7	effd(ω0	effd(ω0	NOUN
ijassa-121	182	8	)	)	PUNCT
ijassa-121	182	9	increases	increase	VERB
ijassa-121	182	10	strictly	strictly	ADV
ijassa-121	182	11	in	in	ADP
ijassa-121	182	12	γ	γ	NOUN
ijassa-121	182	13	and	and	CCONJ
ijassa-121	182	14	ultimately	ultimately	ADV
ijassa-121	182	15	tends	tend	VERB
ijassa-121	182	16	to	to	ADP
ijassa-121	182	17	one	one	NUM
ijassa-121	182	18	.	.	PUNCT
ijassa-121	183	1	fig.1	fig.1	PROPN
ijassa-121	183	2	shows	show	VERB
ijassa-121	183	3	the	the	DET
ijassa-121	183	4	plots	plot	NOUN
ijassa-121	183	5	of	of	ADP
ijassa-121	183	6	these	these	DET
ijassa-121	183	7	two	two	NUM
ijassa-121	183	8	efficiencies	efficiency	NOUN
ijassa-121	183	9	.	.	PUNCT
ijassa-121	184	1	fig.1	fig.1	VERB
ijassa-121	184	2	the	the	DET
ijassa-121	184	3	efficiencies	efficiency	NOUN
ijassa-121	184	4	of	of	ADP
ijassa-121	184	5	effd(ω0	effd(ω0	NOUN
ijassa-121	184	6	)	)	PUNCT
ijassa-121	184	7	and	and	CCONJ
ijassa-121	184	8	effg(ω0	effg(ω0	ADJ
ijassa-121	184	9	)	)	PUNCT
ijassa-121	184	10	with	with	ADP
ijassa-121	184	11	different	different	ADJ
ijassa-121	184	12	λ	λ	PROPN
ijassa-121	184	13	example	example	NOUN
ijassa-121	184	14	2	2	NUM
ijassa-121	184	15	for	for	ADP
ijassa-121	184	16	the	the	DET
ijassa-121	184	17	model	model	NOUN
ijassa-121	184	18	(	(	PUNCT
ijassa-121	184	19	2	2	NUM
ijassa-121	184	20	)	)	PUNCT
ijassa-121	184	21	with	with	ADP
ijassa-121	184	22	mi	mi	PROPN
ijassa-121	184	23	=	=	PROPN
ijassa-121	184	24	m(i	m(i	PROPN
ijassa-121	184	25	=	=	SYM
ijassa-121	184	26	1	1	NUM
ijassa-121	184	27	,	,	PUNCT
ijassa-121	184	28	.	.	PUNCT
ijassa-121	184	29	.	.	PUNCT
ijassa-121	184	30	.	.	PUNCT
ijassa-121	185	1	,	,	PUNCT
ijassa-121	185	2	n),and	n),and	PROPN
ijassa-121	186	1	λ(x	λ(x	X
ijassa-121	186	2	)	)	PUNCT
ijassa-121	186	3	=	=	SYM
ijassa-121	186	4	1	1	NUM
ijassa-121	186	5	1+x	1+x	NUM
ijassa-121	186	6	,	,	PUNCT
ijassa-121	186	7	the	the	DET
ijassa-121	186	8	d-	d-	X
ijassa-121	186	9	,	,	PUNCT
ijassa-121	186	10	g-	g-	X
ijassa-121	186	11	,	,	PUNCT
ijassa-121	186	12	a-	a-	X
ijassa-121	186	13	,	,	PUNCT
ijassa-121	186	14	iand	iand	VERB
ijassa-121	186	15	ds	ds	ADJ
ijassa-121	186	16	-	-	PUNCT
ijassa-121	186	17	optimal	optimal	ADJ
ijassa-121	186	18	proportions	proportion	NOUN
ijassa-121	186	19	as	as	SCONJ
ijassa-121	186	20	follows	follow	VERB
ijassa-121	186	21	:	:	PUNCT
ijassa-121	186	22	ωd	ωd	PROPN
ijassa-121	186	23	=	=	SYM
ijassa-121	186	24	√	√	PROPN
ijassa-121	186	25	2	2	NUM
ijassa-121	187	1	+	+	CCONJ
ijassa-121	187	2	2γ√	2γ√	NUM
ijassa-121	187	3	+	+	NUM
ijassa-121	187	4	γ	γ	NOUN
ijassa-121	187	5	+	+	CCONJ
ijassa-121	187	6	√	√	NUM
ijassa-121	187	7	2	2	NUM
ijassa-121	187	8	+	+	CCONJ
ijassa-121	187	9	2γ	2γ	NOUN
ijassa-121	187	10	,	,	PUNCT
ijassa-121	187	11	ωg	ωg	NOUN
ijassa-121	187	12	=	=	SYM
ijassa-121	187	13	2	2	NUM
ijassa-121	187	14	3	3	NUM
ijassa-121	187	15	,	,	PUNCT
ijassa-121	187	16	ωa	ωa	ADV
ijassa-121	187	17	=	=	NOUN
ijassa-121	187	18	1	1	NUM
ijassa-121	187	19	2	2	NUM
ijassa-121	187	20	,	,	PUNCT
ijassa-121	187	21	ωi	ωi	NOUN
ijassa-121	187	22	=	=	SYM
ijassa-121	187	23	ωds	ωds	NOUN
ijassa-121	187	24	=	=	NOUN
ijassa-121	187	25	2−	2−	NUM
ijassa-121	187	26	√	√	NUM
ijassa-121	187	27	2	2	NUM
ijassa-121	187	28	.	.	PUNCT
ijassa-121	188	1	the	the	DET
ijassa-121	188	2	dand	dand	PROPN
ijassa-121	188	3	g	g	PROPN
ijassa-121	188	4	-	-	PUNCT
ijassa-121	188	5	efficiencies	efficiency	NOUN
ijassa-121	188	6	defined	define	VERB
ijassa-121	188	7	by	by	ADP
ijassa-121	188	8	(	(	PUNCT
ijassa-121	188	9	8)	8)	NUM
ijassa-121	188	10	of	of	ADP
ijassa-121	188	11	the	the	DET
ijassa-121	188	12	equireplicated	equireplicated	ADJ
ijassa-121	188	13	design	design	NOUN
ijassa-121	188	14	ω0	ω0	NOUN
ijassa-121	188	15	=	=	SYM
ijassa-121	188	16	0.5	0.5	NUM
ijassa-121	188	17	are	be	AUX
ijassa-121	188	18	as	as	SCONJ
ijassa-121	188	19	follows	follow	NOUN
ijassa-121	188	20	:	:	PUNCT
ijassa-121	188	21	effd(ω0	effd(ω0	PROPN
ijassa-121	188	22	)	)	PUNCT
ijassa-121	189	1	=	=	SYM
ijassa-121	190	1	√	√	NUM
ijassa-121	190	2	2	2	NUM
ijassa-121	190	3	+	+	NUM
ijassa-121	190	4	2γ	2γ	NOUN
ijassa-121	190	5	+	+	X
ijassa-121	190	6	√	√	NUM
ijassa-121	190	7	2	2	NUM
ijassa-121	190	8	+	+	NUM
ijassa-121	190	9	γ√	γ√	NUM
ijassa-121	190	10	8	8	NUM
ijassa-121	190	11	+	+	NUM
ijassa-121	190	12	6γ	6γ	NOUN
ijassa-121	190	13	,	,	PUNCT
ijassa-121	190	14	effd(ω0	effd(ω0	PROPN
ijassa-121	190	15	)	)	PUNCT
ijassa-121	190	16	=	=	SYM
ijassa-121	191	1	γ	γ	X
ijassa-121	191	2	+	+	CCONJ
ijassa-121	191	3	3	3	NUM
ijassa-121	191	4	γ	γ	NOUN
ijassa-121	191	5	+	+	ADP
ijassa-121	191	6	4	4	NUM
ijassa-121	191	7	.	.	PUNCT
ijassa-121	192	1	it	it	PRON
ijassa-121	192	2	is	be	AUX
ijassa-121	192	3	clear	clear	ADJ
ijassa-121	192	4	that	that	SCONJ
ijassa-121	192	5	effd(ω0	effd(ω0	NOUN
ijassa-121	192	6	)	)	PUNCT
ijassa-121	192	7	decreases	decrease	VERB
ijassa-121	192	8	strictly	strictly	ADV
ijassa-121	192	9	in	in	ADP
ijassa-121	192	10	γ	γ	NOUN
ijassa-121	192	11	and	and	CCONJ
ijassa-121	192	12	ultimately	ultimately	ADV
ijassa-121	192	13	tends	tend	VERB
ijassa-121	192	14	to	to	PART
ijassa-121	192	15	(	(	PUNCT
ijassa-121	192	16	√	√	NUM
ijassa-121	192	17	2	2	NUM
ijassa-121	192	18	+	+	CCONJ
ijassa-121	192	19	1)/	1)/	NUM
ijassa-121	192	20	√	√	NUM
ijassa-121	192	21	6	6	NUM
ijassa-121	192	22	=	=	SYM
ijassa-121	192	23	0.9856	0.9856	NUM
ijassa-121	192	24	,	,	PUNCT
ijassa-121	192	25	and	and	CCONJ
ijassa-121	192	26	effd(ω0	effd(ω0	NOUN
ijassa-121	192	27	)	)	PUNCT
ijassa-121	192	28	increases	increase	VERB
ijassa-121	192	29	strictly	strictly	ADV
ijassa-121	192	30	in	in	ADP
ijassa-121	192	31	γ	γ	NOUN
ijassa-121	192	32	and	and	CCONJ
ijassa-121	192	33	ultimately	ultimately	ADV
ijassa-121	192	34	tends	tend	VERB
ijassa-121	192	35	to	to	ADP
ijassa-121	192	36	one	one	NUM
ijassa-121	192	37	.	.	PUNCT
ijassa-121	193	1	fig.2	fig.2	PROPN
ijassa-121	193	2	shows	show	VERB
ijassa-121	193	3	the	the	DET
ijassa-121	193	4	plots	plot	NOUN
ijassa-121	193	5	of	of	ADP
ijassa-121	193	6	these	these	DET
ijassa-121	193	7	two	two	NUM
ijassa-121	193	8	efficiencies	efficiency	NOUN
ijassa-121	193	9	.	.	PUNCT
ijassa-121	194	1	396	396	NUM
ijassa-121	194	2	jing	jing	PROPN
ijassa-121	194	3	cheng	cheng	PROPN
ijassa-121	194	4	:	:	PUNCT
ijassa-121	194	5	optimal	optimal	ADJ
ijassa-121	194	6	designs	design	NOUN
ijassa-121	194	7	in	in	ADP
ijassa-121	194	8	random	random	ADJ
ijassa-121	194	9	intercept	intercept	NOUN
ijassa-121	194	10	model	model	NOUN
ijassa-121	194	11	with	with	ADP
ijassa-121	194	12	heteroscedastic	heteroscedastic	ADJ
ijassa-121	194	13	errors	error	NOUN
ijassa-121	194	14	fig.2	fig.2	VERB
ijassa-121	194	15	the	the	DET
ijassa-121	194	16	efficiencies	efficiency	NOUN
ijassa-121	194	17	of	of	ADP
ijassa-121	194	18	effd(ω0	effd(ω0	NOUN
ijassa-121	194	19	)	)	PUNCT
ijassa-121	194	20	and	and	CCONJ
ijassa-121	194	21	effg(ω0	effg(ω0	ADJ
ijassa-121	194	22	)	)	PUNCT
ijassa-121	194	23	with	with	ADP
ijassa-121	194	24	different	different	ADJ
ijassa-121	194	25	λ	λ	PROPN
ijassa-121	194	26	example	example	NOUN
ijassa-121	194	27	3	3	NUM
ijassa-121	194	28	for	for	ADP
ijassa-121	194	29	the	the	DET
ijassa-121	194	30	model	model	NOUN
ijassa-121	194	31	(	(	PUNCT
ijassa-121	194	32	2	2	NUM
ijassa-121	194	33	)	)	PUNCT
ijassa-121	194	34	with	with	ADP
ijassa-121	194	35	mi	mi	PROPN
ijassa-121	194	36	=	=	VERB
ijassa-121	194	37	m	m	PROPN
ijassa-121	194	38	(	(	PUNCT
ijassa-121	194	39	i	i	NOUN
ijassa-121	194	40	=	=	NOUN
ijassa-121	194	41	1	1	NUM
ijassa-121	194	42	,	,	PUNCT
ijassa-121	194	43	.	.	PUNCT
ijassa-121	194	44	.	.	PUNCT
ijassa-121	195	1	.	.	PUNCT
ijassa-121	195	2	,	,	PUNCT
ijassa-121	196	1	n	n	CCONJ
ijassa-121	196	2	)	)	PUNCT
ijassa-121	196	3	,	,	PUNCT
ijassa-121	196	4	and	and	CCONJ
ijassa-121	196	5	λ(x	λ(x	X
ijassa-121	196	6	)	)	PUNCT
ijassa-121	197	1	=	=	PUNCT
ijassa-121	197	2	x4	x4	PROPN
ijassa-121	198	1	+	+	PROPN
ijassa-121	198	2	1	1	NUM
ijassa-121	198	3	x2	x2	ADJ
ijassa-121	198	4	+	+	NOUN
ijassa-121	198	5	1	1	NUM
ijassa-121	198	6	,	,	PUNCT
ijassa-121	198	7	the	the	DET
ijassa-121	198	8	d-	d-	X
ijassa-121	198	9	,	,	PUNCT
ijassa-121	198	10	g-	g-	X
ijassa-121	198	11	,	,	PUNCT
ijassa-121	198	12	a-	a-	X
ijassa-121	198	13	,	,	PUNCT
ijassa-121	198	14	iand	iand	VERB
ijassa-121	198	15	ds	ds	ADJ
ijassa-121	198	16	-	-	PUNCT
ijassa-121	198	17	optimal	optimal	ADJ
ijassa-121	198	18	proportions	proportion	NOUN
ijassa-121	198	19	as	as	SCONJ
ijassa-121	198	20	follows	follow	VERB
ijassa-121	198	21	:	:	PUNCT
ijassa-121	198	22	ωd	ωd	ADP
ijassa-121	198	23	=	=	PUNCT
ijassa-121	198	24	ωg	ωg	NOUN
ijassa-121	198	25	=	=	SYM
ijassa-121	198	26	ωi	ωi	PROPN
ijassa-121	198	27	=	=	PUNCT
ijassa-121	198	28	ωds	ωds	NOUN
ijassa-121	198	29	=	=	NOUN
ijassa-121	198	30	1	1	NUM
ijassa-121	198	31	2	2	NUM
ijassa-121	198	32	,	,	PUNCT
ijassa-121	198	33	ωa	ωa	PROPN
ijassa-121	198	34	=	=	SYM
ijassa-121	198	35	√	√	PROPN
ijassa-121	198	36	2−	2−	NUM
ijassa-121	198	37	1	1	NUM
ijassa-121	198	38	.	.	PUNCT
ijassa-121	199	1	that	that	PRON
ijassa-121	199	2	is	is	ADV
ijassa-121	199	3	,	,	PUNCT
ijassa-121	199	4	the	the	DET
ijassa-121	199	5	d-	d-	X
ijassa-121	199	6	,	,	PUNCT
ijassa-121	199	7	g-	g-	X
ijassa-121	199	8	,	,	PUNCT
ijassa-121	199	9	iand	iand	VERB
ijassa-121	199	10	ds	ds	ADJ
ijassa-121	199	11	-	-	PUNCT
ijassa-121	199	12	optimal	optimal	ADJ
ijassa-121	199	13	designs	design	NOUN
ijassa-121	199	14	are	be	AUX
ijassa-121	199	15	all	all	DET
ijassa-121	199	16	the	the	DET
ijassa-121	199	17	equireplicated	equireplicated	ADJ
ijassa-121	199	18	designs	design	NOUN
ijassa-121	199	19	.	.	PUNCT
ijassa-121	200	1	appendix	appendix	ADJ
ijassa-121	200	2	proof	proof	NOUN
ijassa-121	200	3	of	of	ADP
ijassa-121	200	4	lemma	lemma	PROPN
ijassa-121	200	5	1	1	NUM
ijassa-121	200	6	from	from	ADP
ijassa-121	200	7	liski	liski	PROPN
ijassa-121	200	8	et	et	NOUN
ijassa-121	200	9	al[16	al[16	PROPN
ijassa-121	200	10	]	]	X
ijassa-121	200	11	,	,	PUNCT
ijassa-121	200	12	we	we	PRON
ijassa-121	200	13	get	get	VERB
ijassa-121	200	14	m−1(ξ	m−1(ξ	NOUN
ijassa-121	200	15	)	)	PUNCT
ijassa-121	201	1	=	=	SYM
ijassa-121	201	2	m−1	m−1	PROPN
ijassa-121	201	3	0	0	PUNCT
ijassa-121	201	4	(	(	PUNCT
ijassa-121	201	5	ξ	ξ	NOUN
ijassa-121	201	6	)	)	PUNCT
ijassa-121	201	7	+	+	CCONJ
ijassa-121	201	8	(	(	PUNCT
ijassa-121	201	9	d	d	NOUN
ijassa-121	201	10	n	n	CCONJ
ijassa-121	201	11	0	0	NUM
ijassa-121	201	12	0	0	NUM
ijassa-121	201	13	0	0	NUM
ijassa-121	201	14	)	)	PUNCT
ijassa-121	201	15	here	here	ADV
ijassa-121	201	16	m0(ξ	m0(ξ	NOUN
ijassa-121	201	17	)	)	PUNCT
ijassa-121	201	18	is	be	AUX
ijassa-121	201	19	the	the	DET
ijassa-121	201	20	corresponding	corresponding	ADJ
ijassa-121	201	21	generalized	generalized	ADJ
ijassa-121	201	22	information	information	NOUN
ijassa-121	201	23	matrix	matrix	NOUN
ijassa-121	201	24	when	when	SCONJ
ijassa-121	201	25	there	there	PRON
ijassa-121	201	26	are	be	VERB
ijassa-121	201	27	no	no	DET
ijassa-121	201	28	individual	individual	ADJ
ijassa-121	201	29	intercepts	intercept	NOUN
ijassa-121	201	30	,	,	PUNCT
ijassa-121	201	31	i.e.	i.e.	X
ijassa-121	201	32	,	,	PUNCT
ijassa-121	201	33	m0(ξ	m0(ξ	NOUN
ijassa-121	201	34	)	)	PUNCT
ijassa-121	201	35	=	=	SYM
ijassa-121	201	36	mn	mn	PROPN
ijassa-121	201	37	(	(	PUNCT
ijassa-121	201	38	ν0	ν0	PROPN
ijassa-121	201	39	ν1	ν1	NOUN
ijassa-121	201	40	ν1	ν1	NOUN
ijassa-121	201	41	ν2	ν2	NOUN
ijassa-121	201	42	)	)	PUNCT
ijassa-121	201	43	.	.	PUNCT
ijassa-121	202	1	let	let	VERB
ijassa-121	202	2	the	the	DET
ijassa-121	202	3	proportion	proportion	NOUN
ijassa-121	202	4	ω	ω	PROPN
ijassa-121	202	5	in	in	ADP
ijassa-121	202	6	ξ∗	ξ∗	PROPN
ijassa-121	202	7	be	be	AUX
ijassa-121	202	8	of	of	ADP
ijassa-121	202	9	the	the	DET
ijassa-121	202	10	form	form	NOUN
ijassa-121	202	11	ω	ω	NOUN
ijassa-121	202	12	=	=	SYM
ijassa-121	202	13	ν1	ν1	PROPN
ijassa-121	202	14	/	/	SYM
ijassa-121	202	15	λ1	λ1	PROPN
ijassa-121	202	16	.	.	PUNCT
ijassa-121	203	1	it	it	PRON
ijassa-121	203	2	follows	follow	VERB
ijassa-121	203	3	that	that	SCONJ
ijassa-121	203	4	m0(ξ	m0(ξ	ADJ
ijassa-121	203	5	∗	∗	NOUN
ijassa-121	203	6	)	)	PUNCT
ijassa-121	203	7	=	=	SYM
ijassa-121	203	8	mn	mn	PROPN
ijassa-121	203	9	(	(	PUNCT
ijassa-121	203	10	ν∗0	ν∗0	ADJ
ijassa-121	203	11	ν∗1	ν∗1	NOUN
ijassa-121	203	12	ν∗1	ν∗1	NOUN
ijassa-121	203	13	ν∗2	ν∗2	PROPN
ijassa-121	203	14	)	)	PUNCT
ijassa-121	203	15	,	,	PUNCT
ijassa-121	203	16	and	and	CCONJ
ijassa-121	203	17	m0(ξ	m0(ξ	X
ijassa-121	203	18	∗)−m0(ξ	∗)−m0(ξ	X
ijassa-121	203	19	)	)	PUNCT
ijassa-121	203	20	=	=	SYM
ijassa-121	203	21	mn	mn	PROPN
ijassa-121	203	22	(	(	PUNCT
ijassa-121	203	23	ν∗0	ν∗0	ADJ
ijassa-121	203	24	−	−	PROPN
ijassa-121	203	25	ν0	ν0	PROPN
ijassa-121	203	26	0	0	NUM
ijassa-121	203	27	0	0	NUM
ijassa-121	203	28	ν∗2	ν∗2	ADJ
ijassa-121	203	29	−	−	PROPN
ijassa-121	203	30	ν2	ν2	NOUN
ijassa-121	203	31	)	)	PUNCT
ijassa-121	203	32	.	.	PUNCT
ijassa-121	204	1	here	here	ADV
ijassa-121	204	2	ν∗0	ν∗0	ADJ
ijassa-121	204	3	=	=	PUNCT
ijassa-121	205	1	λ1ω	λ1ω	X
ijassa-121	205	2	+	+	NUM
ijassa-121	205	3	λ0(1−	λ0(1−	PROPN
ijassa-121	205	4	ω	ω	NUM
ijassa-121	205	5	)	)	PUNCT
ijassa-121	205	6	and	and	CCONJ
ijassa-121	205	7	ν∗2	ν∗2	ADJ
ijassa-121	205	8	=	=	SYM
ijassa-121	205	9	ν∗1	ν∗1	NOUN
ijassa-121	205	10	=	=	PUNCT
ijassa-121	205	11	λ1ω	λ1ω	NOUN
ijassa-121	205	12	.	.	PUNCT
ijassa-121	206	1	since	since	SCONJ
ijassa-121	206	2	1	1	NUM
ijassa-121	206	3	λ(x	λ(x	PROPN
ijassa-121	206	4	)	)	PUNCT
ijassa-121	206	5	≥	≥	NOUN
ijassa-121	206	6	1−	1−	NUM
ijassa-121	206	7	x	x	SYM
ijassa-121	206	8	λ0	λ0	NOUN
ijassa-121	206	9	+	+	CCONJ
ijassa-121	206	10	x	x	SYM
ijassa-121	206	11	λ1	λ1	ADJ
ijassa-121	206	12	≥	≥	NOUN
ijassa-121	206	13	x	x	SYM
ijassa-121	206	14	λ1	λ1	ADJ
ijassa-121	206	15	,	,	PUNCT
ijassa-121	206	16	advances	advance	NOUN
ijassa-121	206	17	in	in	ADP
ijassa-121	206	18	systems	system	NOUN
ijassa-121	206	19	science	science	NOUN
ijassa-121	206	20	and	and	CCONJ
ijassa-121	206	21	applications	application	NOUN
ijassa-121	206	22	(	(	PUNCT
ijassa-121	206	23	2012	2012	NUM
ijassa-121	206	24	)	)	PUNCT
ijassa-121	206	25	vol.12	vol.12	NOUN
ijassa-121	206	26	no.4	no.4	PROPN
ijassa-121	206	27	397	397	NUM
ijassa-121	206	28	so	so	ADV
ijassa-121	206	29	λ1	λ1	ADJ
ijassa-121	206	30	≥	≥	NOUN
ijassa-121	206	31	xλ(x	xλ(x	PUNCT
ijassa-121	206	32	)	)	PUNCT
ijassa-121	206	33	.	.	PUNCT
ijassa-121	207	1	it	it	PRON
ijassa-121	207	2	implies	imply	VERB
ijassa-121	207	3	0	0	PUNCT
ijassa-121	207	4	<	<	X
ijassa-121	207	5	ω	ω	X
ijassa-121	207	6	<	<	X
ijassa-121	207	7	1	1	NUM
ijassa-121	207	8	.	.	PUNCT
ijassa-121	208	1	[	[	PUNCT
ijassa-121	208	2	m0(ξ	m0(ξ	X
ijassa-121	208	3	∗)−m0(ξ	∗)−m0(ξ	NOUN
ijassa-121	208	4	)	)	PUNCT
ijassa-121	208	5	]	]	PUNCT
ijassa-121	208	6	11	11	NUM
ijassa-121	208	7	=	=	SYM
ijassa-121	208	8	mn	mn	PROPN
ijassa-121	208	9	[	[	PUNCT
ijassa-121	208	10	λ1ω	λ1ω	X
ijassa-121	208	11	+	+	CCONJ
ijassa-121	208	12	λ0(1−	λ0(1−	PROPN
ijassa-121	208	13	ω)−	ω)−	PROPN
ijassa-121	208	14	p∑	p∑	NOUN
ijassa-121	208	15	j=1	j=1	NOUN
ijassa-121	208	16	ωjλ(xj	ωjλ(xj	ADV
ijassa-121	208	17	)	)	PUNCT
ijassa-121	208	18	]	]	PUNCT
ijassa-121	209	1	=	=	SYM
ijassa-121	209	2	mn	mn	PROPN
ijassa-121	209	3	p∑	p∑	PROPN
ijassa-121	209	4	j=1	j=1	PROPN
ijassa-121	210	1	ωj	ωj	ADP
ijassa-121	210	2	[	[	PUNCT
ijassa-121	210	3	λ(xj)xj(λ1	λ(xj)xj(λ1	PROPN
ijassa-121	210	4	−	−	NUM
ijassa-121	210	5	λ0)−	λ0)−	X
ijassa-121	210	6	λ1λ(xj	λ1λ(xj	NOUN
ijassa-121	210	7	)	)	PUNCT
ijassa-121	210	8	+	+	CCONJ
ijassa-121	210	9	λ1λ0	λ1λ0	DET
ijassa-121	210	10	]	]	X
ijassa-121	210	11	condition	condition	NOUN
ijassa-121	210	12	(	(	PUNCT
ijassa-121	210	13	6	6	NUM
ijassa-121	210	14	)	)	PUNCT
ijassa-121	210	15	implies	imply	VERB
ijassa-121	210	16	λ(x)x(λ1	λ(x)x(λ1	PROPN
ijassa-121	210	17	−	−	PROPN
ijassa-121	210	18	λ0)−	λ0)−	PROPN
ijassa-121	210	19	λ1λ(x	λ1λ(x	PROPN
ijassa-121	210	20	)	)	PUNCT
ijassa-121	211	1	+	+	CCONJ
ijassa-121	211	2	λ1λ0	λ1λ0	DET
ijassa-121	211	3	≥	≥	NUM
ijassa-121	211	4	0	0	NUM
ijassa-121	211	5	.	.	PUNCT
ijassa-121	212	1	so	so	ADV
ijassa-121	212	2	we	we	PRON
ijassa-121	212	3	obtain	obtain	VERB
ijassa-121	212	4	[	[	PUNCT
ijassa-121	212	5	m0(ξ	m0(ξ	X
ijassa-121	212	6	∗)−m0(ξ	∗)−m0(ξ	X
ijassa-121	212	7	)	)	PUNCT
ijassa-121	212	8	]	]	PUNCT
ijassa-121	213	1	11	11	NUM
ijassa-121	213	2	≥	≥	NOUN
ijassa-121	213	3	0	0	NUM
ijassa-121	213	4	by	by	ADP
ijassa-121	213	5	ν∗2	ν∗2	ADJ
ijassa-121	213	6	=	=	SYM
ijassa-121	213	7	ν∗1	ν∗1	NOUN
ijassa-121	213	8	=	=	SYM
ijassa-121	213	9	ν1	ν1	NOUN
ijassa-121	213	10	≥	≥	NOUN
ijassa-121	213	11	ν2	ν2	NOUN
ijassa-121	213	12	,	,	PUNCT
ijassa-121	213	13	we	we	PRON
ijassa-121	213	14	have	have	VERB
ijassa-121	213	15	[	[	PUNCT
ijassa-121	213	16	m0(ξ	m0(ξ	X
ijassa-121	213	17	∗)−m0(ξ	∗)−m0(ξ	NOUN
ijassa-121	213	18	)	)	PUNCT
ijassa-121	213	19	]	]	PUNCT
ijassa-121	214	1	22	22	NUM
ijassa-121	214	2	≥	≥	NOUN
ijassa-121	214	3	0	0	NUM
ijassa-121	215	1	so	so	CCONJ
ijassa-121	215	2	we	we	PRON
ijassa-121	215	3	get	get	VERB
ijassa-121	215	4	m0(ξ	m0(ξ	VERB
ijassa-121	215	5	∗	∗	NOUN
ijassa-121	215	6	)	)	PUNCT
ijassa-121	215	7	≥	≥	NOUN
ijassa-121	215	8	m0(ξ	m0(ξ	VERB
ijassa-121	215	9	)	)	PUNCT
ijassa-121	215	10	and	and	CCONJ
ijassa-121	215	11	hence	hence	ADV
ijassa-121	215	12	m(ξ∗	m(ξ∗	NOUN
ijassa-121	215	13	)	)	PUNCT
ijassa-121	215	14	≥	≥	NOUN
ijassa-121	215	15	m0(ξ	m0(ξ	NOUN
ijassa-121	215	16	)	)	PUNCT
ijassa-121	215	17	.	.	PUNCT
ijassa-121	216	1	acknowledgements	acknowledgement	NOUN
ijassa-121	216	2	this	this	DET
ijassa-121	216	3	work	work	NOUN
ijassa-121	216	4	was	be	AUX
ijassa-121	216	5	partially	partially	ADV
ijassa-121	216	6	supported	support	VERB
ijassa-121	216	7	by	by	ADP
ijassa-121	216	8	a	a	DET
ijassa-121	216	9	nsfc	nsfc	NOUN
ijassa-121	216	10	grant	grant	NOUN
ijassa-121	216	11	(	(	PUNCT
ijassa-121	216	12	11071168	11071168	NUM
ijassa-121	216	13	)	)	PUNCT
ijassa-121	216	14	,	,	PUNCT
ijassa-121	216	15	special	special	ADJ
ijassa-121	216	16	funds	fund	NOUN
ijassa-121	216	17	for	for	ADP
ijassa-121	216	18	doctoral	doctoral	ADJ
ijassa-121	216	19	authorities	authority	NOUN
ijassa-121	216	20	of	of	ADP
ijassa-121	216	21	education	education	PROPN
ijassa-121	216	22	ministry	ministry	PROPN
ijassa-121	216	23	(	(	PUNCT
ijassa-121	216	24	20103127110002	20103127110002	NUM
ijassa-121	216	25	)	)	PUNCT
ijassa-121	216	26	,	,	PUNCT
ijassa-121	216	27	e	e	X
ijassa-121	216	28	-	-	NOUN
ijassa-121	216	29	institutes	institutes	NOUN
ijassa-121	216	30	of	of	ADP
ijassa-121	216	31	shanghai	shanghai	PROPN
ijassa-121	216	32	municipal	municipal	PROPN
ijassa-121	216	33	education	education	PROPN
ijassa-121	216	34	commission	commission	PROPN
ijassa-121	216	35	(	(	PUNCT
ijassa-121	216	36	e03004	e03004	PROPN
ijassa-121	216	37	)	)	PUNCT
ijassa-121	216	38	,	,	PUNCT
ijassa-121	216	39	shanghai	shanghai	PROPN
ijassa-121	216	40	leading	lead	VERB
ijassa-121	216	41	academic	academic	ADJ
ijassa-121	216	42	discipline	discipline	NOUN
ijassa-121	216	43	project	project	NOUN
ijassa-121	216	44	(	(	PUNCT
ijassa-121	216	45	s30405	s30405	PROPN
ijassa-121	216	46	)	)	PUNCT
ijassa-121	216	47	,	,	PUNCT
ijassa-121	216	48	the	the	DET
ijassa-121	216	49	innovation	innovation	NOUN
ijassa-121	216	50	program	program	NOUN
ijassa-121	216	51	of	of	ADP
ijassa-121	216	52	shanghai	shanghai	PROPN
ijassa-121	216	53	municipal	municipal	PROPN
ijassa-121	216	54	education	education	PROPN
ijassa-121	216	55	commission	commission	PROPN
ijassa-121	216	56	(	(	PUNCT
ijassa-121	216	57	11zz116	11zz116	ADV
ijassa-121	216	58	)	)	PUNCT
ijassa-121	216	59	,	,	PUNCT
ijassa-121	216	60	and	and	CCONJ
ijassa-121	216	61	the	the	DET
ijassa-121	216	62	scientific	scientific	ADJ
ijassa-121	216	63	research	research	NOUN
ijassa-121	216	64	foundation	foundation	PROPN
ijassa-121	216	65	of	of	ADP
ijassa-121	216	66	chaohu	chaohu	PROPN
ijassa-121	216	67	college	college	PROPN
ijassa-121	216	68	.	.	PUNCT
ijassa-121	217	1	references	reference	NOUN
ijassa-121	217	2	[	[	X
ijassa-121	217	3	1	1	NUM
ijassa-121	217	4	]	]	X
ijassa-121	217	5	n.t	n.t	PROPN
ijassa-121	217	6	.	.	PROPN
ijassa-121	217	7	longford	longford	PROPN
ijassa-121	217	8	(	(	PUNCT
ijassa-121	217	9	1993	1993	NUM
ijassa-121	217	10	)	)	PUNCT
ijassa-121	217	11	,	,	PUNCT
ijassa-121	217	12	random	random	ADJ
ijassa-121	217	13	coefficient	coefficient	NOUN
ijassa-121	217	14	regression	regression	NOUN
ijassa-121	217	15	models	model	NOUN
ijassa-121	217	16	,	,	PUNCT
ijassa-121	217	17	clarendon	clarendon	PROPN
ijassa-121	217	18	press	press	NOUN
ijassa-121	217	19	,	,	PUNCT
ijassa-121	217	20	oxford	oxford	PROPN
ijassa-121	217	21	.	.	PUNCT
ijassa-121	218	1	[	[	X
ijassa-121	218	2	2	2	NUM
ijassa-121	218	3	]	]	X
ijassa-121	218	4	d.	d.	PROPN
ijassa-121	218	5	pena	pena	PROPN
ijassa-121	218	6	,	,	PUNCT
ijassa-121	218	7	v.	v.	PROPN
ijassa-121	218	8	yohai	yohai	PROPN
ijassa-121	218	9	(	(	PUNCT
ijassa-121	218	10	2006	2006	NUM
ijassa-121	218	11	)	)	PUNCT
ijassa-121	218	12	,	,	PUNCT
ijassa-121	218	13	“	"	PUNCT
ijassa-121	218	14	a	a	DET
ijassa-121	218	15	dirichlet	dirichlet	PROPN
ijassa-121	218	16	random	random	ADJ
ijassa-121	218	17	coefficient	coefficient	NOUN
ijassa-121	218	18	regression	regression	NOUN
ijassa-121	218	19	model	model	NOUN
ijassa-121	218	20	for	for	ADP
ijassa-121	218	21	quality	quality	NOUN
ijassa-121	218	22	indicators	indicator	NOUN
ijassa-121	218	23	”	"	PUNCT
ijassa-121	218	24	,	,	PUNCT
ijassa-121	218	25	journal	journal	NOUN
ijassa-121	218	26	of	of	ADP
ijassa-121	218	27	statistical	statistical	ADJ
ijassa-121	218	28	planning	planning	NOUN
ijassa-121	218	29	and	and	CCONJ
ijassa-121	218	30	inference	inference	NOUN
ijassa-121	218	31	,	,	PUNCT
ijassa-121	218	32	vol.136	vol.136	NOUN
ijassa-121	218	33	,	,	PUNCT
ijassa-121	218	34	pp.942	pp.942	NOUN
ijassa-121	218	35	-	-	PUNCT
ijassa-121	218	36	961	961	NUM
ijassa-121	218	37	.	.	PUNCT
ijassa-121	219	1	[	[	X
ijassa-121	219	2	3	3	X
ijassa-121	219	3	]	]	X
ijassa-121	219	4	f.	f.	PROPN
ijassa-121	219	5	yu	yu	PROPN
ijassa-121	219	6	(	(	PUNCT
ijassa-121	219	7	2007	2007	NUM
ijassa-121	219	8	)	)	PUNCT
ijassa-121	219	9	,	,	PUNCT
ijassa-121	219	10	“	"	PUNCT
ijassa-121	219	11	quadratic	quadratic	ADJ
ijassa-121	219	12	design	design	NOUN
ijassa-121	219	13	criterion	criterion	NOUN
ijassa-121	219	14	for	for	ADP
ijassa-121	219	15	nonlinear	nonlinear	ADJ
ijassa-121	219	16	models	model	NOUN
ijassa-121	219	17	”	"	PUNCT
ijassa-121	219	18	,	,	PUNCT
ijassa-121	219	19	advances	advance	NOUN
ijassa-121	219	20	in	in	ADP
ijassa-121	219	21	systems	system	NOUN
ijassa-121	219	22	science	science	NOUN
ijassa-121	219	23	and	and	CCONJ
ijassa-121	219	24	applications	application	NOUN
ijassa-121	219	25	,	,	PUNCT
ijassa-121	219	26	vol.7	vol.7	PROPN
ijassa-121	219	27	,	,	PUNCT
ijassa-121	219	28	no.2	no.2	PROPN
ijassa-121	219	29	,	,	PUNCT
ijassa-121	219	30	pp.155	pp.155	PROPN
ijassa-121	219	31	-	-	NOUN
ijassa-121	219	32	160	160	NUM
ijassa-121	219	33	.	.	PUNCT
ijassa-121	220	1	[	[	X
ijassa-121	220	2	4	4	X
ijassa-121	220	3	]	]	PUNCT
ijassa-121	220	4	t.	t.	NOUN
ijassa-121	220	5	schmelter	schmelter	NOUN
ijassa-121	220	6	(	(	PUNCT
ijassa-121	220	7	2007	2007	NUM
ijassa-121	220	8	)	)	PUNCT
ijassa-121	220	9	,	,	PUNCT
ijassa-121	220	10	“	"	PUNCT
ijassa-121	220	11	the	the	DET
ijassa-121	220	12	optimality	optimality	NOUN
ijassa-121	220	13	of	of	ADP
ijassa-121	220	14	single	single	ADJ
ijassa-121	220	15	-	-	PUNCT
ijassa-121	220	16	group	group	NOUN
ijassa-121	220	17	designs	design	NOUN
ijassa-121	220	18	for	for	ADP
ijassa-121	220	19	certain	certain	ADJ
ijassa-121	220	20	mixed	mixed	ADJ
ijassa-121	220	21	models	model	NOUN
ijassa-121	220	22	”	"	PUNCT
ijassa-121	220	23	,	,	PUNCT
ijassa-121	220	24	metrika	metrika	X
ijassa-121	220	25	,	,	PUNCT
ijassa-121	220	26	vol.65	vol.65	NOUN
ijassa-121	220	27	,	,	PUNCT
ijassa-121	220	28	pp.183	pp.183	NOUN
ijassa-121	220	29	-	-	PUNCT
ijassa-121	220	30	193	193	NUM
ijassa-121	220	31	.	.	PUNCT
ijassa-121	221	1	398	398	NUM
ijassa-121	221	2	jing	jing	ADJ
ijassa-121	221	3	cheng	cheng	PROPN
ijassa-121	221	4	:	:	PUNCT
ijassa-121	221	5	optimal	optimal	ADJ
ijassa-121	221	6	designs	design	NOUN
ijassa-121	221	7	in	in	ADP
ijassa-121	221	8	random	random	ADJ
ijassa-121	221	9	intercept	intercept	NOUN
ijassa-121	221	10	model	model	NOUN
ijassa-121	221	11	with	with	ADP
ijassa-121	221	12	heteroscedastic	heteroscedastic	ADJ
ijassa-121	221	13	errors	error	NOUN
ijassa-121	221	14	[	[	X
ijassa-121	221	15	5	5	X
ijassa-121	221	16	]	]	PUNCT
ijassa-121	221	17	t.	t.	NOUN
ijassa-121	221	18	schmelter	schmelter	NOUN
ijassa-121	221	19	(	(	PUNCT
ijassa-121	221	20	2007	2007	NUM
ijassa-121	221	21	)	)	PUNCT
ijassa-121	221	22	,	,	PUNCT
ijassa-121	221	23	“	"	PUNCT
ijassa-121	221	24	consideration	consideration	NOUN
ijassa-121	221	25	on	on	ADP
ijassa-121	221	26	group	group	NOUN
ijassa-121	221	27	-	-	PUNCT
ijassa-121	221	28	wise	wise	ADJ
ijassa-121	221	29	identical	identical	ADJ
ijassa-121	221	30	designs	design	NOUN
ijassa-121	221	31	for	for	ADP
ijassa-121	221	32	linear	linear	ADJ
ijassa-121	221	33	mixed	mixed	ADJ
ijassa-121	221	34	models	model	NOUN
ijassa-121	221	35	”	"	PUNCT
ijassa-121	221	36	,	,	PUNCT
ijassa-121	221	37	journal	journal	NOUN
ijassa-121	221	38	of	of	ADP
ijassa-121	221	39	statistical	statistical	ADJ
ijassa-121	221	40	planning	planning	NOUN
ijassa-121	221	41	and	and	CCONJ
ijassa-121	221	42	inference	inference	NOUN
ijassa-121	221	43	,	,	PUNCT
ijassa-121	221	44	vol.137	vol.137	PRON
ijassa-121	221	45	,	,	PUNCT
ijassa-121	221	46	pp.4003	pp.4003	PROPN
ijassa-121	221	47	-	-	NOUN
ijassa-121	221	48	4010	4010	NUM
ijassa-121	221	49	.	.	PUNCT
ijassa-121	222	1	[	[	X
ijassa-121	222	2	6	6	NUM
ijassa-121	222	3	]	]	PUNCT
ijassa-121	222	4	r.	r.	NOUN
ijassa-121	222	5	schwabe	schwabe	NOUN
ijassa-121	222	6	,	,	PUNCT
ijassa-121	222	7	t.	t.	PROPN
ijassa-121	222	8	schmelter	schmelter	NOUN
ijassa-121	222	9	.	.	PUNCT
ijassa-121	223	1	(	(	PUNCT
ijassa-121	223	2	2008	2008	NUM
ijassa-121	223	3	)	)	PUNCT
ijassa-121	223	4	,	,	PUNCT
ijassa-121	223	5	“	"	PUNCT
ijassa-121	223	6	on	on	ADP
ijassa-121	223	7	optimal	optimal	ADJ
ijassa-121	223	8	designs	design	NOUN
ijassa-121	223	9	in	in	ADP
ijassa-121	223	10	random	random	ADJ
ijassa-121	223	11	intercept	intercept	NOUN
ijassa-121	223	12	models	model	NOUN
ijassa-121	223	13	”	"	PUNCT
ijassa-121	223	14	,	,	PUNCT
ijassa-121	223	15	tatar	tatar	NOUN
ijassa-121	223	16	mountains	mountain	NOUN
ijassa-121	223	17	mathematical	mathematical	ADJ
ijassa-121	223	18	publications	publication	NOUN
ijassa-121	223	19	,	,	PUNCT
ijassa-121	223	20	vol.39	vol.39	NOUN
ijassa-121	223	21	,	,	PUNCT
ijassa-121	223	22	pp.189	pp.189	PROPN
ijassa-121	223	23	-	-	PUNCT
ijassa-121	223	24	195	195	NUM
ijassa-121	223	25	.	.	PUNCT
ijassa-121	224	1	[	[	X
ijassa-121	224	2	7	7	X
ijassa-121	224	3	]	]	X
ijassa-121	224	4	t.	t.	NOUN
ijassa-121	224	5	schmelter	schmelter	NOUN
ijassa-121	224	6	,	,	PUNCT
ijassa-121	224	7	et	et	PROPN
ijassa-121	224	8	al	al	PROPN
ijassa-121	224	9	.	.	PUNCT
ijassa-121	224	10	(	(	PUNCT
ijassa-121	224	11	2007	2007	NUM
ijassa-121	224	12	)	)	PUNCT
ijassa-121	224	13	,	,	PUNCT
ijassa-121	224	14	“	"	PUNCT
ijassa-121	224	15	some	some	DET
ijassa-121	224	16	curiosities	curiosity	NOUN
ijassa-121	224	17	in	in	ADP
ijassa-121	224	18	optimal	optimal	ADJ
ijassa-121	224	19	designs	design	NOUN
ijassa-121	224	20	for	for	ADP
ijassa-121	224	21	random	random	ADJ
ijassa-121	224	22	slopes	slope	NOUN
ijassa-121	224	23	”	"	PUNCT
ijassa-121	224	24	,	,	PUNCT
ijassa-121	224	25	in	in	ADP
ijassa-121	224	26	:	:	PUNCT
ijassa-121	224	27	contributions	contribution	NOUN
ijassa-121	224	28	to	to	ADP
ijassa-121	224	29	statistics	statistic	NOUN
ijassa-121	224	30	.	.	PUNCT
ijassa-121	225	1	moda	moda	PROPN
ijassa-121	225	2	8	8	NUM
ijassa-121	225	3	advances	advance	NOUN
ijassa-121	225	4	in	in	ADP
ijassa-121	225	5	modeloriented	modeloriented	ADJ
ijassa-121	225	6	design	design	NOUN
ijassa-121	225	7	and	and	CCONJ
ijassa-121	225	8	analysis	analysis	NOUN
ijassa-121	225	9	,	,	PUNCT
ijassa-121	225	10	physica	physica	NOUN
ijassa-121	225	11	-	-	PUNCT
ijassa-121	225	12	verlag	verlag	PROPN
ijassa-121	225	13	heidelberg	heidelberg	PROPN
ijassa-121	225	14	,	,	PUNCT
ijassa-121	225	15	pp.189	pp.189	PROPN
ijassa-121	225	16	-	-	PUNCT
ijassa-121	225	17	195	195	NUM
ijassa-121	225	18	.	.	PUNCT
ijassa-121	226	1	[	[	X
ijassa-121	226	2	8	8	NUM
ijassa-121	226	3	]	]	PUNCT
ijassa-121	226	4	a.	a.	NOUN
ijassa-121	226	5	luoma	luoma	PROPN
ijassa-121	226	6	,	,	PUNCT
ijassa-121	226	7	et	et	PROPN
ijassa-121	226	8	al	al	PROPN
ijassa-121	226	9	.	.	PUNCT
ijassa-121	227	1	(	(	PUNCT
ijassa-121	227	2	2007	2007	NUM
ijassa-121	227	3	)	)	PUNCT
ijassa-121	228	1	,	,	PUNCT
ijassa-121	228	2	“	"	PUNCT
ijassa-121	228	3	optimal	optimal	ADJ
ijassa-121	228	4	designs	design	NOUN
ijassa-121	228	5	in	in	ADP
ijassa-121	228	6	random	random	ADJ
ijassa-121	228	7	coefficient	coefficient	NOUN
ijassa-121	228	8	cubic	cubic	ADJ
ijassa-121	228	9	regression	regression	NOUN
ijassa-121	228	10	models	model	NOUN
ijassa-121	228	11	”	"	PUNCT
ijassa-121	228	12	,	,	PUNCT
ijassa-121	228	13	journal	journal	NOUN
ijassa-121	228	14	of	of	ADP
ijassa-121	228	15	statistical	statistical	ADJ
ijassa-121	228	16	planning	planning	NOUN
ijassa-121	228	17	and	and	CCONJ
ijassa-121	228	18	inference	inference	NOUN
ijassa-121	228	19	,	,	PUNCT
ijassa-121	228	20	vol.137	vol.137	PRON
ijassa-121	228	21	,	,	PUNCT
ijassa-121	228	22	pp.3611	pp.3611	PROPN
ijassa-121	228	23	-	-	NOUN
ijassa-121	228	24	3617	3617	NUM
ijassa-121	228	25	.	.	PUNCT
ijassa-121	229	1	[	[	X
ijassa-121	229	2	9	9	NUM
ijassa-121	229	3	]	]	PUNCT
ijassa-121	229	4	m.	m.	NOUN
ijassa-121	229	5	entholzner	entholzner	NOUN
ijassa-121	229	6	,	,	PUNCT
ijassa-121	229	7	et	et	PROPN
ijassa-121	229	8	al	al	PROPN
ijassa-121	229	9	.	.	PUNCT
ijassa-121	229	10	(	(	PUNCT
ijassa-121	229	11	2005	2005	NUM
ijassa-121	229	12	)	)	PUNCT
ijassa-121	229	13	,	,	PUNCT
ijassa-121	229	14	“	"	PUNCT
ijassa-121	229	15	a	a	DET
ijassa-121	229	16	note	note	NOUN
ijassa-121	229	17	on	on	ADP
ijassa-121	229	18	designs	design	NOUN
ijassa-121	229	19	for	for	ADP
ijassa-121	229	20	estimating	estimate	VERB
ijassa-121	229	21	population	population	NOUN
ijassa-121	229	22	parameter	parameter	NOUN
ijassa-121	229	23	”	"	PUNCT
ijassa-121	229	24	,	,	PUNCT
ijassa-121	229	25	listy	listy	PROPN
ijassa-121	229	26	biometryczne	biometryczne	NOUN
ijassa-121	229	27	-	-	PUNCT
ijassa-121	229	28	biometrical	biometrical	ADJ
ijassa-121	229	29	letters	letter	NOUN
ijassa-121	229	30	,	,	PUNCT
ijassa-121	229	31	vol.42	vol.42	ADP
ijassa-121	229	32	,	,	PUNCT
ijassa-121	229	33	pp.25	pp.25	PROPN
ijassa-121	229	34	-	-	NOUN
ijassa-121	229	35	41	41	NUM
ijassa-121	229	36	.	.	PUNCT
ijassa-121	230	1	[	[	X
ijassa-121	230	2	10	10	NUM
ijassa-121	230	3	]	]	X
ijassa-121	230	4	l.k	l.k	PROPN
ijassa-121	230	5	.	.	PROPN
ijassa-121	230	6	debusho	debusho	PROPN
ijassa-121	230	7	,	,	PUNCT
ijassa-121	230	8	l.m	l.m	PROPN
ijassa-121	230	9	.	.	PROPN
ijassa-121	230	10	haines	haine	NOUN
ijassa-121	230	11	(	(	PUNCT
ijassa-121	230	12	2007	2007	NUM
ijassa-121	230	13	)	)	PUNCT
ijassa-121	230	14	,	,	PUNCT
ijassa-121	230	15	“	"	PUNCT
ijassa-121	230	16	vand	vand	NOUN
ijassa-121	230	17	d	d	ADJ
ijassa-121	230	18	-	-	ADJ
ijassa-121	230	19	optimal	optimal	ADJ
ijassa-121	230	20	population	population	NOUN
ijassa-121	230	21	designs	design	NOUN
ijassa-121	230	22	for	for	ADP
ijassa-121	230	23	the	the	DET
ijassa-121	230	24	simple	simple	ADJ
ijassa-121	230	25	linear	linear	ADJ
ijassa-121	230	26	regression	regression	NOUN
ijassa-121	230	27	model	model	NOUN
ijassa-121	230	28	with	with	ADP
ijassa-121	230	29	a	a	DET
ijassa-121	230	30	random	random	ADJ
ijassa-121	230	31	intercept	intercept	NOUN
ijassa-121	230	32	term	term	NOUN
ijassa-121	230	33	”	"	PUNCT
ijassa-121	230	34	,	,	PUNCT
ijassa-121	230	35	journal	journal	NOUN
ijassa-121	230	36	of	of	ADP
ijassa-121	230	37	statistical	statistical	ADJ
ijassa-121	230	38	planning	planning	NOUN
ijassa-121	230	39	and	and	CCONJ
ijassa-121	230	40	inference	inference	NOUN
ijassa-121	230	41	,	,	PUNCT
ijassa-121	230	42	vol.138	vol.138	NOUN
ijassa-121	230	43	,	,	PUNCT
ijassa-121	230	44	pp.1116	pp.1116	PROPN
ijassa-121	230	45	-	-	NOUN
ijassa-121	230	46	1130	1130	NUM
ijassa-121	230	47	.	.	PUNCT
ijassa-121	231	1	[	[	X
ijassa-121	231	2	11	11	NUM
ijassa-121	231	3	]	]	X
ijassa-121	231	4	g.	g.	PROPN
ijassa-121	231	5	z.	z.	PROPN
ijassa-121	231	6	wang	wang	PROPN
ijassa-121	231	7	,	,	PUNCT
ijassa-121	231	8	j.	j.	PROPN
ijassa-121	231	9	zhao	zhao	PROPN
ijassa-121	231	10	,	,	PUNCT
ijassa-121	231	11	j.	j.	PROPN
ijassa-121	231	12	b.	b.	PROPN
ijassa-121	231	13	chen	chen	PROPN
ijassa-121	231	14	(	(	PUNCT
ijassa-121	231	15	2006	2006	NUM
ijassa-121	231	16	)	)	PUNCT
ijassa-121	231	17	,	,	PUNCT
ijassa-121	231	18	“	"	PUNCT
ijassa-121	231	19	the	the	DET
ijassa-121	231	20	uniform	uniform	NOUN
ijassa-121	231	21	design	design	NOUN
ijassa-121	231	22	modeling	modeling	NOUN
ijassa-121	231	23	and	and	CCONJ
ijassa-121	231	24	analysis	analysis	NOUN
ijassa-121	231	25	”	"	PUNCT
ijassa-121	231	26	,	,	PUNCT
ijassa-121	231	27	advances	advance	NOUN
ijassa-121	231	28	in	in	ADP
ijassa-121	231	29	systems	system	NOUN
ijassa-121	231	30	science	science	NOUN
ijassa-121	231	31	and	and	CCONJ
ijassa-121	231	32	applications	application	NOUN
ijassa-121	231	33	,	,	PUNCT
ijassa-121	231	34	vol.6	vol.6	PROPN
ijassa-121	231	35	,	,	PUNCT
ijassa-121	231	36	no.4	no.4	PROPN
ijassa-121	231	37	,	,	PUNCT
ijassa-121	231	38	pp.699	pp.699	PROPN
ijassa-121	231	39	-	-	PUNCT
ijassa-121	231	40	703	703	NUM
ijassa-121	231	41	.	.	PUNCT
ijassa-121	232	1	[	[	X
ijassa-121	232	2	12	12	NUM
ijassa-121	232	3	]	]	X
ijassa-121	232	4	s.h	s.h	PROPN
ijassa-121	232	5	.	.	PROPN
ijassa-121	232	6	yu	yu	PROPN
ijassa-121	232	7	(	(	PUNCT
ijassa-121	232	8	2007	2007	NUM
ijassa-121	232	9	)	)	PUNCT
ijassa-121	232	10	,	,	PUNCT
ijassa-121	232	11	“	"	PUNCT
ijassa-121	232	12	the	the	DET
ijassa-121	232	13	linear	linear	ADJ
ijassa-121	232	14	minimax	minimax	NOUN
ijassa-121	232	15	estimator	estimator	NOUN
ijassa-121	232	16	of	of	ADP
ijassa-121	232	17	stochastic	stochastic	ADJ
ijassa-121	232	18	regression	regression	NOUN
ijassa-121	232	19	coefficients	coefficient	NOUN
ijassa-121	232	20	and	and	CCONJ
ijassa-121	232	21	parameters	parameter	NOUN
ijassa-121	232	22	under	under	ADP
ijassa-121	232	23	quadratic	quadratic	ADJ
ijassa-121	232	24	loss	loss	NOUN
ijassa-121	232	25	function	function	NOUN
ijassa-121	232	26	”	"	PUNCT
ijassa-121	232	27	,	,	PUNCT
ijassa-121	232	28	statistics	statistic	NOUN
ijassa-121	232	29	and	and	CCONJ
ijassa-121	232	30	probability	probability	NOUN
ijassa-121	232	31	letters	letter	NOUN
ijassa-121	232	32	,	,	PUNCT
ijassa-121	232	33	vol.77	vol.77	ADV
ijassa-121	232	34	,	,	PUNCT
ijassa-121	232	35	pp.54	pp.54	NOUN
ijassa-121	232	36	-	-	PUNCT
ijassa-121	232	37	62	62	NUM
ijassa-121	232	38	.	.	PUNCT
ijassa-121	233	1	[	[	X
ijassa-121	233	2	13	13	NUM
ijassa-121	233	3	]	]	X
ijassa-121	233	4	g.y	g.y	PROPN
ijassa-121	233	5	.	.	PUNCT
ijassa-121	233	6	wen	wen	PROPN
ijassa-121	233	7	,	,	PUNCT
ijassa-121	233	8	et	et	PROPN
ijassa-121	233	9	al	al	PROPN
ijassa-121	233	10	.	.	PUNCT
ijassa-121	233	11	(	(	PUNCT
ijassa-121	233	12	2008	2008	NUM
ijassa-121	233	13	)	)	PUNCT
ijassa-121	233	14	,	,	PUNCT
ijassa-121	233	15	“	"	PUNCT
ijassa-121	233	16	orthogonal	orthogonal	ADJ
ijassa-121	233	17	experimental	experimental	ADJ
ijassa-121	233	18	design	design	NOUN
ijassa-121	233	19	in	in	ADP
ijassa-121	233	20	matrix	matrix	NOUN
ijassa-121	233	21	form	form	NOUN
ijassa-121	233	22	and	and	CCONJ
ijassa-121	233	23	its	its	PRON
ijassa-121	233	24	application	application	NOUN
ijassa-121	233	25	to	to	ADP
ijassa-121	233	26	the	the	DET
ijassa-121	233	27	introduction	introduction	NOUN
ijassa-121	233	28	selection	selection	NOUN
ijassa-121	233	29	of	of	ADP
ijassa-121	233	30	thymus	thymus	ADJ
ijassa-121	233	31	genus	genus	NOUN
ijassa-121	233	32	”	"	PUNCT
ijassa-121	233	33	,	,	PUNCT
ijassa-121	233	34	advances	advance	NOUN
ijassa-121	233	35	in	in	ADP
ijassa-121	233	36	systems	system	NOUN
ijassa-121	233	37	science	science	NOUN
ijassa-121	233	38	and	and	CCONJ
ijassa-121	233	39	applications	application	NOUN
ijassa-121	233	40	,	,	PUNCT
ijassa-121	233	41	vol.8	vol.8	PROPN
ijassa-121	233	42	,	,	PUNCT
ijassa-121	233	43	no.3	no.3	NOUN
ijassa-121	233	44	,	,	PUNCT
ijassa-121	233	45	pp.437	pp.437	NOUN
ijassa-121	233	46	-	-	PUNCT
ijassa-121	233	47	446	446	NUM
ijassa-121	233	48	.	.	PUNCT
ijassa-121	234	1	[	[	X
ijassa-121	234	2	14	14	NUM
ijassa-121	234	3	]	]	X
ijassa-121	234	4	f.	f.	PROPN
ijassa-121	234	5	pukelsheim	pukelsheim	PROPN
ijassa-121	234	6	(	(	PUNCT
ijassa-121	234	7	1993	1993	NUM
ijassa-121	234	8	)	)	PUNCT
ijassa-121	234	9	,	,	PUNCT
ijassa-121	234	10	optimal	optimal	ADJ
ijassa-121	234	11	design	design	NOUN
ijassa-121	234	12	of	of	ADP
ijassa-121	234	13	experiment	experiment	NOUN
ijassa-121	234	14	,	,	PUNCT
ijassa-121	234	15	johnwiley	johnwiley	NOUN
ijassa-121	234	16	,	,	PUNCT
ijassa-121	234	17	new	new	PROPN
ijassa-121	234	18	york	york	PROPN
ijassa-121	234	19	.	.	PUNCT
ijassa-121	235	1	[	[	X
ijassa-121	235	2	15	15	NUM
ijassa-121	235	3	]	]	X
ijassa-121	235	4	f.c	f.c	PROPN
ijassa-121	235	5	.	.	PROPN
ijassa-121	235	6	chang	chang	PROPN
ijassa-121	235	7	(	(	PUNCT
ijassa-121	235	8	2005	2005	NUM
ijassa-121	235	9	)	)	PUNCT
ijassa-121	235	10	,	,	PUNCT
ijassa-121	235	11	“	"	PUNCT
ijassa-121	235	12	d	d	X
ijassa-121	235	13	-	-	ADJ
ijassa-121	235	14	optimal	optimal	ADJ
ijassa-121	235	15	designs	design	NOUN
ijassa-121	235	16	for	for	ADP
ijassa-121	235	17	weighted	weight	VERB
ijassa-121	235	18	polynomial	polynomial	ADJ
ijassa-121	235	19	regression	regression	NOUN
ijassa-121	235	20	–	–	PUNCT
ijassa-121	235	21	a	a	DET
ijassa-121	235	22	functional	functional	ADJ
ijassa-121	235	23	approach	approach	NOUN
ijassa-121	235	24	”	"	PUNCT
ijassa-121	235	25	,	,	PUNCT
ijassa-121	235	26	annals	annal	NOUN
ijassa-121	235	27	of	of	ADP
ijassa-121	235	28	the	the	DET
ijassa-121	235	29	institute	institute	NOUN
ijassa-121	235	30	of	of	ADP
ijassa-121	235	31	statistical	statistical	ADJ
ijassa-121	235	32	mathematics	mathematic	NOUN
ijassa-121	235	33	,	,	PUNCT
ijassa-121	235	34	vol.57	vol.57	NOUN
ijassa-121	235	35	,	,	PUNCT
ijassa-121	235	36	no.4	no.4	PROPN
ijassa-121	235	37	,	,	PUNCT
ijassa-121	235	38	pp.833	pp.833	NOUN
ijassa-121	235	39	-	-	PUNCT
ijassa-121	235	40	844	844	NUM
ijassa-121	235	41	.	.	PUNCT
ijassa-121	236	1	[	[	X
ijassa-121	236	2	16	16	NUM
ijassa-121	236	3	]	]	X
ijassa-121	236	4	e.p	e.p	PROPN
ijassa-121	236	5	.	.	PROPN
ijassa-121	236	6	liski	liski	PROPN
ijassa-121	236	7	,	,	PUNCT
ijassa-121	236	8	n.k	n.k	PROPN
ijassa-121	236	9	.	.	PROPN
ijassa-121	236	10	mandal	mandal	PROPN
ijassa-121	236	11	,	,	PUNCT
ijassa-121	236	12	k.r	k.r	PROPN
ijassa-121	236	13	.	.	PROPN
ijassa-121	236	14	shah	shah	PROPN
ijassa-121	236	15	,	,	PUNCT
ijassa-121	236	16	b.k	b.k	PROPN
ijassa-121	236	17	.	.	PROPN
ijassa-121	236	18	sinha	sinha	PROPN
ijassa-121	236	19	(	(	PUNCT
ijassa-121	236	20	2002	2002	NUM
ijassa-121	236	21	)	)	PUNCT
ijassa-121	236	22	,	,	PUNCT
ijassa-121	236	23	topics	topic	NOUN
ijassa-121	236	24	in	in	ADP
ijassa-121	236	25	optimal	optimal	ADJ
ijassa-121	236	26	designs	design	NOUN
ijassa-121	236	27	,	,	PUNCT
ijassa-121	236	28	springer	springer	NOUN
ijassa-121	236	29	,	,	PUNCT
ijassa-121	236	30	new	new	PROPN
ijassa-121	236	31	york	york	PROPN
ijassa-121	236	32	.	.	PUNCT
ijassa-121	237	1	corresponding	correspond	VERB
ijassa-121	237	2	author	author	NOUN
ijassa-121	237	3	nicholas	nicholas	PROPN
ijassa-121	237	4	nechval	nechval	PROPN
ijassa-121	237	5	can	can	AUX
ijassa-121	237	6	be	be	AUX
ijassa-121	237	7	contacted	contact	VERB
ijassa-121	237	8	at:yue2@shnu.edu.cn	at:yue2@shnu.edu.cn	NOUN