id	sid	tid	token	lemma	pos
iajs-898	1	1	ibn	ibn	PROPN
iajs-898	1	2	alhaitham	alhaitham	NOUN
iajs-898	1	3	j.	j.	PROPN
iajs-898	2	1	fo	fo	ADP
iajs-898	2	2	r	r	NOUN
iajs-898	2	3	pure	pure	ADJ
iajs-898	2	4	&	&	CCONJ
iajs-898	2	5	appl	appl	PROPN
iajs-898	2	6	.	.	PUNCT
iajs-898	3	1	sc	sc	PROPN
iajs-898	3	2	i.	i.	PROPN
iajs-898	3	3	vo	vo	PROPN
iajs-898	3	4	l.23	l.23	PROPN
iajs-898	3	5	(	(	PUNCT
iajs-898	3	6	3	3	NUM
iajs-898	3	7	)	)	PUNCT
iajs-898	3	8	2010	2010	NUM
iajs-898	3	9	bayesian	bayesian	NOUN
iajs-898	3	10	analyses	analysis	NOUN
iajs-898	3	11	of	of	ADP
iajs-898	3	12	ridge	ridge	NOUN
iajs-898	3	13	regression	regression	PROPN
iajs-898	3	14	prooblems	prooblem	NOUN
iajs-898	3	15	h.	h.	PROPN
iajs-898	3	16	m.	m.	PROPN
iajs-898	3	17	gorgees	gorgees	PROPN
iajs-898	3	18	department	department	PROPN
iajs-898	3	19	of	of	ADP
iajs-898	3	20	mathematics	mathematics	PROPN
iajs-898	3	21	,	,	PUNCT
iajs-898	3	22	college	college	NOUN
iajs-898	3	23	of	of	ADP
iajs-898	3	24	education	education	PROPN
iajs-898	3	25	ibn	ibn	PROPN
iajs-898	3	26	-	-	PUNCT
iajs-898	3	27	al	al	PROPN
iajs-898	3	28	-	-	PUNCT
iajs-898	3	29	haitham	haitham	PROPN
iajs-898	3	30	,	,	PUNCT
iajs-898	3	31	university	university	PROPN
iajs-898	3	32	of	of	ADP
iajs-898	3	33	baghdad	baghdad	PROPN
iajs-898	3	34	abstract	abstract	NOUN
iajs-898	3	35	:	:	PUNCT
iajs-898	3	36	a	a	DET
iajs-898	3	37	bayesian	bayesian	NOUN
iajs-898	3	38	formulation	formulation	NOUN
iajs-898	3	39	of	of	ADP
iajs-898	3	40	the	the	DET
iajs-898	3	41	ridge	ridge	NOUN
iajs-898	3	42	regression	regression	PROPN
iajs-898	3	43	problem	problem	NOUN
iajs-898	3	44	is	be	AUX
iajs-898	3	45	considerd	considerd	ADJ
iajs-898	3	46	,	,	PUNCT
iajs-898	3	47	which	which	PRON
iajs-898	3	48	derives	derive	VERB
iajs-898	3	49	from	from	ADP
iajs-898	3	50	a	a	DET
iajs-898	3	51	direct	direct	ADJ
iajs-898	3	52	specification	specification	NOUN
iajs-898	3	53	of	of	ADP
iajs-898	3	54	prior	prior	ADJ
iajs-898	3	55	informations	information	NOUN
iajs-898	3	56	about	about	ADP
iajs-898	3	57	parameters	parameter	NOUN
iajs-898	3	58	of	of	ADP
iajs-898	3	59	general	general	ADJ
iajs-898	3	60	linear	linear	PROPN
iajs-898	3	61	regression	regression	NOUN
iajs-898	3	62	model	model	NOUN
iajs-898	3	63	when	when	SCONJ
iajs-898	3	64	data	datum	NOUN
iajs-898	3	65	suffer	suffer	VERB
iajs-898	3	66	from	from	ADP
iajs-898	3	67	a	a	DET
iajs-898	3	68	high	high	ADJ
iajs-898	3	69	degree	degree	NOUN
iajs-898	3	70	of	of	ADP
iajs-898	3	71	multicollinearity	multicollinearity	NOUN
iajs-898	3	72	.a	.a	NOUN
iajs-898	4	1	new	new	ADJ
iajs-898	4	2	approach	approach	NOUN
iajs-898	4	3	for	for	ADP
iajs-898	4	4	deriving	derive	VERB
iajs-898	4	5	the	the	DET
iajs-898	4	6	conventional	conventional	ADJ
iajs-898	4	7	estimator	estimator	NOUN
iajs-898	4	8	for	for	ADP
iajs-898	4	9	the	the	DET
iajs-898	4	10	ridge	ridge	NOUN
iajs-898	4	11	parameter	parameter	NOUN
iajs-898	4	12	proposed	propose	VERB
iajs-898	4	13	by	by	ADP
iajs-898	4	14	hoerl	hoerl	NOUN
iajs-898	4	15	and	and	CCONJ
iajs-898	4	16	kennard	kennard	NOUN
iajs-898	4	17	(	(	PUNCT
iajs-898	4	18	1970	1970	NUM
iajs-898	4	19	)	)	PUNCT
iajs-898	4	20	as	as	ADV
iajs-898	4	21	well	well	ADV
iajs-898	4	22	as	as	ADP
iajs-898	4	23	bayesian	bayesian	NOUN
iajs-898	4	24	estimator	estimator	NOUN
iajs-898	4	25	are	be	AUX
iajs-898	4	26	presented	present	VERB
iajs-898	4	27	.	.	PUNCT
iajs-898	5	1	a	a	DET
iajs-898	5	2	numerical	numerical	ADJ
iajs-898	5	3	example	example	NOUN
iajs-898	5	4	is	be	AUX
iajs-898	5	5	studied	study	VERB
iajs-898	5	6	in	in	ADP
iajs-898	5	7	order	order	NOUN
iajs-898	5	8	to	to	PART
iajs-898	5	9	compare	compare	VERB
iajs-898	5	10	the	the	DET
iajs-898	5	11	performance	performance	NOUN
iajs-898	5	12	of	of	ADP
iajs-898	5	13	these	these	DET
iajs-898	5	14	estimators	estimator	NOUN
iajs-898	5	15	.	.	PUNCT
iajs-898	6	1	introduction	introduction	NOUN
iajs-898	6	2	:	:	PUNCT
iajs-898	6	3	the	the	DET
iajs-898	6	4	problem	problem	NOUN
iajs-898	6	5	of	of	ADP
iajs-898	6	6	multicollinearity	multicollinearity	NOUN
iajs-898	6	7	exists	exist	VERB
iajs-898	6	8	when	when	SCONJ
iajs-898	6	9	there	there	PRON
iajs-898	6	10	exists	exist	VERB
iajs-898	6	11	a	a	DET
iajs-898	6	12	linear	linear	ADJ
iajs-898	6	13	relationship	relationship	NOUN
iajs-898	6	14	or	or	CCONJ
iajs-898	6	15	an	an	DET
iajs-898	6	16	approximate	approximate	ADJ
iajs-898	6	17	linear	linear	PROPN
iajs-898	6	18	relationship	relationship	NOUN
iajs-898	6	19	among	among	ADP
iajs-898	6	20	two	two	NUM
iajs-898	6	21	or	or	CCONJ
iajs-898	6	22	more	more	ADJ
iajs-898	6	23	explanatory	explanatory	ADJ
iajs-898	6	24	variables	variable	NOUN
iajs-898	6	25	.	.	PUNCT
iajs-898	7	1	multicollinearity	multicollinearity	NOUN
iajs-898	7	2	can	can	AUX
iajs-898	7	3	be	be	AUX
iajs-898	7	4	thought	think	VERB
iajs-898	7	5	of	of	ADP
iajs-898	7	6	as	as	ADP
iajs-898	7	7	a	a	DET
iajs-898	7	8	situation	situation	NOUN
iajs-898	7	9	where	where	SCONJ
iajs-898	7	10	two	two	NUM
iajs-898	7	11	or	or	CCONJ
iajs-898	7	12	more	more	ADJ
iajs-898	7	13	explanatory	explanatory	ADJ
iajs-898	7	14	variables	variable	NOUN
iajs-898	7	15	in	in	ADP
iajs-898	7	16	the	the	DET
iajs-898	7	17	data	datum	NOUN
iajs-898	7	18	set	set	VERB
iajs-898	7	19	move	move	VERB
iajs-898	7	20	together	together	ADV
iajs-898	7	21	.	.	PUNCT
iajs-898	8	1	as	as	ADP
iajs-898	8	2	a	a	DET
iajs-898	8	3	consequence	consequence	NOUN
iajs-898	8	4	it	it	PRON
iajs-898	8	5	is	be	AUX
iajs-898	8	6	impossible	impossible	ADJ
iajs-898	8	7	to	to	PART
iajs-898	8	8	use	use	VERB
iajs-898	8	9	this	this	DET
iajs-898	8	10	data	datum	NOUN
iajs-898	8	11	set	set	VERB
iajs-898	8	12	to	to	PART
iajs-898	8	13	decide	decide	VERB
iajs-898	8	14	which	which	PRON
iajs-898	8	15	of	of	ADP
iajs-898	8	16	the	the	DET
iajs-898	8	17	explanatory	explanatory	ADJ
iajs-898	8	18	variables	variable	NOUN
iajs-898	8	19	is	be	AUX
iajs-898	8	20	producing	produce	VERB
iajs-898	8	21	the	the	DET
iajs-898	8	22	observed	observed	ADJ
iajs-898	8	23	change	change	NOUN
iajs-898	8	24	in	in	ADP
iajs-898	8	25	the	the	DET
iajs-898	8	26	response	response	NOUN
iajs-898	8	27	variable	variable	NOUN
iajs-898	8	28	.	.	PUNCT
iajs-898	9	1	no	no	DET
iajs-898	9	2	treatment	treatment	NOUN
iajs-898	9	3	of	of	ADP
iajs-898	9	4	the	the	DET
iajs-898	9	5	data	datum	NOUN
iajs-898	9	6	or	or	CCONJ
iajs-898	9	7	transformation	transformation	NOUN
iajs-898	9	8	of	of	ADP
iajs-898	9	9	the	the	DET
iajs-898	9	10	model	model	NOUN
iajs-898	9	11	will	will	AUX
iajs-898	9	12	cure	cure	VERB
iajs-898	9	13	this	this	DET
iajs-898	9	14	deficiency	deficiency	NOUN
iajs-898	9	15	.	.	PUNCT
iajs-898	10	1	consequently	consequently	ADV
iajs-898	10	2	,	,	PUNCT
iajs-898	10	3	the	the	DET
iajs-898	10	4	best	good	ADJ
iajs-898	10	5	way	way	NOUN
iajs-898	10	6	to	to	PART
iajs-898	10	7	deal	deal	VERB
iajs-898	10	8	with	with	ADP
iajs-898	10	9	multicollinearity	multicollinearity	NOUN
iajs-898	10	10	may	may	AUX
iajs-898	10	11	be	be	AUX
iajs-898	10	12	to	to	PART
iajs-898	10	13	find	find	VERB
iajs-898	10	14	a	a	DET
iajs-898	10	15	different	different	ADJ
iajs-898	10	16	data	datum	NOUN
iajs-898	10	17	set	set	VERB
iajs-898	10	18	,	,	PUNCT
iajs-898	10	19	or	or	CCONJ
iajs-898	10	20	additional	additional	ADJ
iajs-898	10	21	data	datum	NOUN
iajs-898	10	22	to	to	PART
iajs-898	10	23	break	break	VERB
iajs-898	10	24	the	the	DET
iajs-898	10	25	association	association	NOUN
iajs-898	10	26	between	between	ADP
iajs-898	10	27	the	the	DET
iajs-898	10	28	related	related	ADJ
iajs-898	10	29	variables	variable	NOUN
iajs-898	10	30	.	.	PUNCT
iajs-898	11	1	however	however	ADV
iajs-898	11	2	some	some	DET
iajs-898	11	3	multicollinearity	multicollinearity	NOUN
iajs-898	11	4	is	be	AUX
iajs-898	11	5	nearly	nearly	ADV
iajs-898	11	6	always	always	ADV
iajs-898	11	7	present	present	ADJ
iajs-898	11	8	,	,	PUNCT
iajs-898	11	9	but	but	CCONJ
iajs-898	11	10	the	the	DET
iajs-898	11	11	important	important	ADJ
iajs-898	11	12	point	point	NOUN
iajs-898	11	13	is	be	AUX
iajs-898	11	14	whether	whether	SCONJ
iajs-898	11	15	the	the	DET
iajs-898	11	16	multicollinearity	multicollinearity	NOUN
iajs-898	11	17	is	be	AUX
iajs-898	11	18	serious	serious	ADJ
iajs-898	11	19	enough	enough	ADV
iajs-898	11	20	to	to	PART
iajs-898	11	21	cause	cause	VERB
iajs-898	11	22	appreciable	appreciable	ADJ
iajs-898	11	23	damage	damage	NOUN
iajs-898	11	24	to	to	ADP
iajs-898	11	25	the	the	DET
iajs-898	11	26	regression	regression	NOUN
iajs-898	11	27	.	.	PUNCT
iajs-898	12	1	indicators	indicator	NOUN
iajs-898	12	2	of	of	ADP
iajs-898	12	3	multicollinearity	multicollinearity	NOUN
iajs-898	12	4	include	include	VERB
iajs-898	12	5	a	a	DET
iajs-898	12	6	low	low	ADJ
iajs-898	12	7	determinant	determinant	NOUN
iajs-898	12	8	of	of	ADP
iajs-898	12	9	the	the	DET
iajs-898	12	10	information	information	NOUN
iajs-898	12	11	matrix	matrix	NOUN
iajs-898	12	12	,	,	PUNCT
iajs-898	12	13	a	a	DET
iajs-898	12	14	very	very	ADV
iajs-898	12	15	high	high	ADJ
iajs-898	12	16	correlation	correlation	NOUN
iajs-898	12	17	among	among	ADP
iajs-898	12	18	two	two	NUM
iajs-898	12	19	or	or	CCONJ
iajs-898	12	20	more	more	ADV
iajs-898	12	21	explanatory	explanatory	ADJ
iajs-898	12	22	variables	variable	NOUN
iajs-898	12	23	,	,	PUNCT
iajs-898	12	24	very	very	ADV
iajs-898	12	25	high	high	ADJ
iajs-898	12	26	correlations	correlation	NOUN
iajs-898	12	27	among	among	ADP
iajs-898	12	28	two	two	NUM
iajs-898	12	29	or	or	CCONJ
iajs-898	12	30	more	more	ADV
iajs-898	12	31	estimated	estimated	ADJ
iajs-898	12	32	coefficients	coefficient	NOUN
iajs-898	12	33	,	,	PUNCT
iajs-898	12	34	and	and	CCONJ
iajs-898	12	35	significant	significant	ADJ
iajs-898	12	36	regression	regression	NOUN
iajs-898	12	37	of	of	ADP
iajs-898	12	38	one	one	NUM
iajs-898	12	39	explanatory	explanatory	ADJ
iajs-898	12	40	variable	variable	NOUN
iajs-898	12	41	on	on	ADP
iajs-898	12	42	one	one	NUM
iajs-898	12	43	or	or	CCONJ
iajs-898	12	44	more	more	ADJ
iajs-898	12	45	explanatory	explanatory	ADJ
iajs-898	12	46	variables	variable	NOUN
iajs-898	12	47	.	.	PUNCT
iajs-898	13	1	key	key	ADJ
iajs-898	13	2	words	word	NOUN
iajs-898	13	3	:	:	PUNCT
iajs-898	13	4	linear	linear	ADJ
iajs-898	13	5	regressioin	regressioin	ADJ
iajs-898	13	6	model	model	NOUN
iajs-898	13	7	,	,	PUNCT
iajs-898	13	8	multicollinearity	multicollinearity	NOUN
iajs-898	13	9	,	,	PUNCT
iajs-898	13	10	ridge	ridge	NOUN
iajs-898	13	11	regression	regression	NOUN
iajs-898	13	12	,	,	PUNCT
iajs-898	13	13	generalized	generalized	ADJ
iajs-898	13	14	shrinkage	shrinkage	NOUN
iajs-898	13	15	estimators	estimator	NOUN
iajs-898	13	16	,	,	PUNCT
iajs-898	13	17	bayesian	bayesian	NOUN
iajs-898	13	18	estimator	estimator	NOUN
iajs-898	13	19	,	,	PUNCT
iajs-898	13	20	singular	singular	ADJ
iajs-898	13	21	value	value	NOUN
iajs-898	13	22	decomposition	decomposition	NOUN
iajs-898	13	23	.	.	PUNCT
iajs-898	14	1	ihjpas	ihjpa	VERB
iajs-898	14	2	ibn	ibn	PROPN
iajs-898	14	3	alhaitham	alhaitham	PROPN
iajs-898	15	1	j.	j.	PROPN
iajs-898	16	1	fo	fo	ADP
iajs-898	16	2	r	r	NOUN
iajs-898	16	3	pure	pure	ADJ
iajs-898	16	4	&	&	CCONJ
iajs-898	16	5	appl	appl	PROPN
iajs-898	16	6	.	.	PUNCT
iajs-898	17	1	sc	sc	PROPN
iajs-898	17	2	i.	i.	PROPN
iajs-898	17	3	vo	vo	PROPN
iajs-898	18	1	l.23	l.23	PROPN
iajs-898	18	2	(	(	PUNCT
iajs-898	18	3	3	3	NUM
iajs-898	18	4	)	)	PUNCT
iajs-898	18	5	2010	2010	NUM
iajs-898	18	6	this	this	DET
iajs-898	18	7	paper	paper	NOUN
iajs-898	18	8	deals	deal	NOUN
iajs-898	18	9	with	with	ADP
iajs-898	18	10	multicollinearity	multicollinearity	NOUN
iajs-898	18	11	in	in	ADP
iajs-898	18	12	the	the	DET
iajs-898	18	13	classical	classical	ADJ
iajs-898	18	14	linear	linear	PROPN
iajs-898	18	15	regression	regression	NOUN
iajs-898	18	16	model	model	NOUN
iajs-898	18	17	y	y	PROPN
iajs-898	18	18	=	=	PROPN
iajs-898	18	19	xβ+u	xβ+u	PROPN
iajs-898	18	20	…	…	PUNCT
iajs-898	18	21	..	..	PUNCT
iajs-898	18	22	(	(	PUNCT
iajs-898	18	23	1	1	X
iajs-898	18	24	)	)	PUNCT
iajs-898	18	25	where	where	SCONJ
iajs-898	18	26	y	y	PROPN
iajs-898	18	27	is	be	AUX
iajs-898	18	28	an	an	DET
iajs-898	18	29	(	(	PUNCT
iajs-898	18	30	of	of	ADP
iajs-898	18	31	observations	observation	NOUN
iajs-898	18	32	on	on	ADP
iajs-898	18	33	the	the	DET
iajs-898	18	34	response	response	NOUN
iajs-898	18	35	variable	variable	NOUN
iajs-898	18	36	,	,	PUNCT
iajs-898	18	37	x	x	PUNCT
iajs-898	18	38	=	=	PUNCT
iajs-898	18	39	(	(	PUNCT
iajs-898	18	40	is	be	AUX
iajs-898	18	41	an	an	DET
iajs-898	18	42	(	(	PUNCT
iajs-898	18	43	n	n	NOUN
iajs-898	18	44	matrix	matrix	NOUN
iajs-898	18	45	and	and	CCONJ
iajs-898	18	46	of	of	ADP
iajs-898	18	47	full	full	ADJ
iajs-898	18	48	column	column	NOUN
iajs-898	18	49	rank	rank	NOUN
iajs-898	18	50	,	,	PUNCT
iajs-898	18	51	β	β	X
iajs-898	18	52	is	be	AUX
iajs-898	18	53	a	a	DET
iajs-898	18	54	(	(	PUNCT
iajs-898	18	55	p	p	NOUN
iajs-898	18	56	parameter	parameter	NOUN
iajs-898	18	57	vector	vector	NOUN
iajs-898	18	58	(	(	PUNCT
iajs-898	18	59	vector	vector	NOUN
iajs-898	18	60	of	of	ADP
iajs-898	18	61	unknown	unknown	ADJ
iajs-898	18	62	regression	regression	NOUN
iajs-898	18	63	coefficients	coefficient	NOUN
iajs-898	18	64	)	)	PUNCT
iajs-898	18	65	and	and	CCONJ
iajs-898	18	66	u	u	NOUN
iajs-898	18	67	is	be	AUX
iajs-898	18	68	an	an	DET
iajs-898	18	69	(	(	PUNCT
iajs-898	18	70	n	n	PRON
iajs-898	18	71	vector	vector	NOUN
iajs-898	18	72	of	of	ADP
iajs-898	18	73	random	random	ADJ
iajs-898	18	74	disturbances	disturbance	NOUN
iajs-898	18	75	,	,	PUNCT
iajs-898	18	76	e(u)=0	e(u)=0	NUM
iajs-898	18	77	and	and	CCONJ
iajs-898	18	78	var(u)=	var(u)=	NUM
iajs-898	18	79	i	i	PROPN
iajs-898	18	80	,	,	PUNCT
iajs-898	18	81	and	and	CCONJ
iajs-898	18	82	both	both	DET
iajs-898	18	83	β	β	NOUN
iajs-898	18	84	and	and	CCONJ
iajs-898	18	85	are	be	AUX
iajs-898	18	86	unknown	unknown	ADJ
iajs-898	18	87	.	.	PUNCT
iajs-898	19	1	the	the	DET
iajs-898	19	2	least	least	ADJ
iajs-898	19	3	squares	square	NOUN
iajs-898	19	4	estimator	estimator	NOUN
iajs-898	19	5	of	of	ADP
iajs-898	19	6	β	β	PROPN
iajs-898	19	7	is	be	AUX
iajs-898	19	8	:	:	PUNCT
iajs-898	19	9	(	(	PUNCT
iajs-898	19	10	see[1	see[1	X
iajs-898	19	11	]	]	PUNCT
iajs-898	19	12	)	)	PUNCT
iajs-898	19	13	=	=	SYM
iajs-898	19	14	x'y	x'y	PRON
iajs-898	19	15	…	…	PUNCT
iajs-898	19	16	..	..	PUNCT
iajs-898	19	17	(	(	PUNCT
iajs-898	19	18	2	2	X
iajs-898	19	19	)	)	PUNCT
iajs-898	19	20	where	where	SCONJ
iajs-898	19	21	denote	denote	VERB
iajs-898	19	22	the	the	DET
iajs-898	19	23	least	least	ADJ
iajs-898	19	24	squares	square	NOUN
iajs-898	19	25	estimator	estimator	NOUN
iajs-898	19	26	of	of	ADP
iajs-898	19	27	β	β	PROPN
iajs-898	19	28	.	.	PUNCT
iajs-898	20	1	the	the	DET
iajs-898	20	2	two	two	NUM
iajs-898	20	3	key	key	ADJ
iajs-898	20	4	properties	property	NOUN
iajs-898	20	5	of	of	ADP
iajs-898	20	6	are	be	AUX
iajs-898	20	7	that	that	SCONJ
iajs-898	20	8	it	it	PRON
iajs-898	20	9	is	be	AUX
iajs-898	20	10	unbiased	unbiased	ADJ
iajs-898	20	11	,	,	PUNCT
iajs-898	20	12	e	e	NOUN
iajs-898	20	13	(	(	PUNCT
iajs-898	20	14	)	)	PUNCT
iajs-898	20	15	=	=	SYM
iajs-898	20	16	β	β	NOUN
iajs-898	20	17	,	,	PUNCT
iajs-898	20	18	and	and	CCONJ
iajs-898	20	19	that	that	SCONJ
iajs-898	20	20	it	it	PRON
iajs-898	20	21	has	have	VERB
iajs-898	20	22	minimum	minimum	ADJ
iajs-898	20	23	variance	variance	NOUN
iajs-898	20	24	among	among	ADP
iajs-898	20	25	all	all	DET
iajs-898	20	26	linear	linear	ADJ
iajs-898	20	27	unbiased	unbiased	ADJ
iajs-898	20	28	estimators.the	estimators.the	DET
iajs-898	20	29	mean	mean	ADJ
iajs-898	20	30	square	square	ADJ
iajs-898	20	31	error	error	NOUN
iajs-898	20	32	of	of	ADP
iajs-898	20	33	is	be	AUX
iajs-898	20	34	:	:	PUNCT
iajs-898	20	35	mse	mse	X
iajs-898	20	36	(	(	PUNCT
iajs-898	20	37	)	)	PUNCT
iajs-898	20	38	=	=	SYM
iajs-898	20	39	…	…	PUNCT
iajs-898	20	40	…	…	PUNCT
iajs-898	20	41	(	(	PUNCT
iajs-898	20	42	3	3	NUM
iajs-898	20	43	)	)	PUNCT
iajs-898	20	44	(	(	PUNCT
iajs-898	20	45	see[2	see[2	NUM
iajs-898	20	46	]	]	PUNCT
iajs-898	20	47	)	)	PUNCT
iajs-898	20	48	where	where	SCONJ
iajs-898	20	49	"	"	PUNCT
iajs-898	20	50	s	s	VERB
iajs-898	20	51	are	be	AUX
iajs-898	20	52	the	the	DET
iajs-898	20	53	eigenvalues	eigenvalue	NOUN
iajs-898	20	54	of	of	ADP
iajs-898	20	55	x'x	x'x	PROPN
iajs-898	20	56	and	and	CCONJ
iajs-898	20	57	…	…	PUNCT
iajs-898	20	58	..	..	PUNCT
iajs-898	21	1	>	>	X
iajs-898	21	2	0	0	PUNCT
iajs-898	21	3	..	..	PUNCT
iajs-898	22	1	if	if	SCONJ
iajs-898	22	2	the	the	DET
iajs-898	22	3	smallest	small	ADJ
iajs-898	22	4	eigenvalue	eigenvalue	NOUN
iajs-898	22	5	of	of	ADP
iajs-898	22	6	x'x	x'x	PROPN
iajs-898	22	7	is	be	AUX
iajs-898	22	8	very	very	ADV
iajs-898	22	9	much	much	ADV
iajs-898	22	10	smaller	small	ADJ
iajs-898	22	11	than	than	ADP
iajs-898	22	12	1	1	NUM
iajs-898	22	13	,	,	PUNCT
iajs-898	22	14	then	then	ADV
iajs-898	22	15	a	a	DET
iajs-898	22	16	seriously	seriously	ADV
iajs-898	22	17	ill_conditioned	ill_conditione	VERB
iajs-898	22	18	(	(	PUNCT
iajs-898	22	19	or	or	CCONJ
iajs-898	22	20	multicollinearity	multicollinearity	NOUN
iajs-898	22	21	)	)	PUNCT
iajs-898	22	22	problem	problem	NOUN
iajs-898	22	23	arises	arise	VERB
iajs-898	22	24	.	.	PUNCT
iajs-898	23	1	thus	thus	ADV
iajs-898	23	2	,	,	PUNCT
iajs-898	23	3	for	for	ADP
iajs-898	23	4	ill_conditioned	ill_conditioned	ADJ
iajs-898	23	5	data	datum	NOUN
iajs-898	23	6	,	,	PUNCT
iajs-898	23	7	the	the	DET
iajs-898	23	8	least	least	ADJ
iajs-898	23	9	squares	square	NOUN
iajs-898	23	10	solution	solution	NOUN
iajs-898	23	11	yields	yield	NOUN
iajs-898	23	12	coefficients	coefficient	VERB
iajs-898	23	13	whose	whose	DET
iajs-898	23	14	absolute	absolute	ADJ
iajs-898	23	15	values	value	NOUN
iajs-898	23	16	are	be	AUX
iajs-898	23	17	too	too	ADV
iajs-898	23	18	large	large	ADJ
iajs-898	23	19	and	and	CCONJ
iajs-898	23	20	whose	whose	DET
iajs-898	23	21	signs	sign	NOUN
iajs-898	23	22	may	may	AUX
iajs-898	23	23	actually	actually	ADV
iajs-898	23	24	reverse	reverse	VERB
iajs-898	23	25	with	with	ADP
iajs-898	23	26	negligible	negligible	ADJ
iajs-898	23	27	changes	change	NOUN
iajs-898	23	28	in	in	ADP
iajs-898	23	29	the	the	DET
iajs-898	23	30	data	datum	NOUN
iajs-898	23	31	.	.	PUNCT
iajs-898	24	1	that	that	PRON
iajs-898	24	2	is	be	AUX
iajs-898	24	3	in	in	ADP
iajs-898	24	4	the	the	DET
iajs-898	24	5	case	case	NOUN
iajs-898	24	6	of	of	ADP
iajs-898	24	7	multicollinearity	multicollinearity	NOUN
iajs-898	24	8	the	the	DET
iajs-898	24	9	least	least	ADJ
iajs-898	24	10	squares	square	NOUN
iajs-898	24	11	estimator	estimator	NOUN
iajs-898	24	12	can	can	AUX
iajs-898	24	13	be	be	AUX
iajs-898	24	14	poor	poor	ADJ
iajs-898	24	15	in	in	ADP
iajs-898	24	16	terms	term	NOUN
iajs-898	24	17	of	of	ADP
iajs-898	24	18	various	various	ADJ
iajs-898	24	19	mean	mean	NOUN
iajs-898	24	20	squared	square	VERB
iajs-898	24	21	error	error	NOUN
iajs-898	24	22	criterion	criterion	NOUN
iajs-898	24	23	.	.	PUNCT
iajs-898	25	1	consequently	consequently	ADV
iajs-898	25	2	,	,	PUNCT
iajs-898	25	3	a	a	DET
iajs-898	25	4	great	great	ADJ
iajs-898	25	5	deal	deal	NOUN
iajs-898	25	6	of	of	ADP
iajs-898	25	7	work	work	NOUN
iajs-898	25	8	has	have	AUX
iajs-898	25	9	been	be	AUX
iajs-898	25	10	done	do	VERB
iajs-898	25	11	to	to	PART
iajs-898	25	12	construct	construct	VERB
iajs-898	25	13	alternatives	alternative	NOUN
iajs-898	25	14	to	to	ADP
iajs-898	25	15	the	the	DET
iajs-898	25	16	least	least	ADJ
iajs-898	25	17	squares	square	NOUN
iajs-898	25	18	estimator	estimator	NOUN
iajs-898	25	19	when	when	SCONJ
iajs-898	25	20	multicollinearity	multicollinearity	NOUN
iajs-898	25	21	is	be	AUX
iajs-898	25	22	present	present	ADJ
iajs-898	25	23	.	.	PUNCT
iajs-898	26	1	in	in	ADP
iajs-898	26	2	the	the	DET
iajs-898	26	3	seventies	seventy	NOUN
iajs-898	26	4	hoerl	hoerl	VERB
iajs-898	26	5	and	and	CCONJ
iajs-898	26	6	kennard	kennard	NOUN
iajs-898	26	7	introduced	introduce	VERB
iajs-898	26	8	a	a	DET
iajs-898	26	9	class	class	NOUN
iajs-898	26	10	of	of	ADP
iajs-898	26	11	biased	biased	ADJ
iajs-898	26	12	estimators	estimator	NOUN
iajs-898	26	13	for	for	ADP
iajs-898	26	14	parameters	parameter	NOUN
iajs-898	26	15	in	in	ADP
iajs-898	26	16	general	general	ADJ
iajs-898	26	17	linear	linear	PROPN
iajs-898	26	18	regression	regression	NOUN
iajs-898	26	19	model	model	NOUN
iajs-898	26	20	labeled	label	VERB
iajs-898	26	21	ridge	ridge	NOUN
iajs-898	26	22	estimators	estimator	NOUN
iajs-898	26	23	as	as	ADP
iajs-898	26	24	a	a	DET
iajs-898	26	25	rival	rival	NOUN
iajs-898	26	26	to	to	ADP
iajs-898	26	27	the	the	DET
iajs-898	26	28	least	least	ADJ
iajs-898	26	29	squares	square	NOUN
iajs-898	26	30	estimator	estimator	NOUN
iajs-898	26	31	when	when	SCONJ
iajs-898	26	32	sample	sample	NOUN
iajs-898	26	33	data	datum	NOUN
iajs-898	26	34	are	be	AUX
iajs-898	26	35	affected	affect	VERB
iajs-898	26	36	by	by	ADP
iajs-898	26	37	a	a	DET
iajs-898	26	38	high	high	ADJ
iajs-898	26	39	degree	degree	NOUN
iajs-898	26	40	of	of	ADP
iajs-898	26	41	multicollinearity	multicollinearity	NOUN
iajs-898	26	42	.	.	PUNCT
iajs-898	27	1	the	the	DET
iajs-898	27	2	outhers	outher	NOUN
iajs-898	27	3	show	show	VERB
iajs-898	27	4	that	that	SCONJ
iajs-898	27	5	in	in	ADP
iajs-898	27	6	any	any	DET
iajs-898	27	7	given	give	VERB
iajs-898	27	8	problem	problem	NOUN
iajs-898	27	9	there	there	PRON
iajs-898	27	10	is	be	VERB
iajs-898	27	11	at	at	ADV
iajs-898	27	12	least	least	ADV
iajs-898	27	13	one	one	NUM
iajs-898	27	14	member	member	NOUN
iajs-898	27	15	of	of	ADP
iajs-898	27	16	this	this	DET
iajs-898	27	17	class	class	NOUN
iajs-898	27	18	which	which	PRON
iajs-898	27	19	has	have	VERB
iajs-898	27	20	total	total	ADJ
iajs-898	27	21	mean	mean	ADJ
iajs-898	27	22	square	square	ADJ
iajs-898	27	23	error	error	NOUN
iajs-898	27	24	smaller	small	ADJ
iajs-898	27	25	than	than	ADP
iajs-898	27	26	the	the	DET
iajs-898	27	27	total	total	ADJ
iajs-898	27	28	variance	variance	NOUN
iajs-898	27	29	of	of	ADP
iajs-898	27	30	the	the	DET
iajs-898	27	31	corresponding	corresponding	ADJ
iajs-898	27	32	least	least	ADJ
iajs-898	27	33	squares	square	NOUN
iajs-898	27	34	estimator	estimator	NOUN
iajs-898	27	35	.	.	PUNCT
iajs-898	28	1	the	the	DET
iajs-898	28	2	ridge	ridge	NOUN
iajs-898	28	3	estimator	estimator	NOUN
iajs-898	28	4	depends	depend	VERB
iajs-898	28	5	crucially	crucially	ADV
iajs-898	28	6	upon	upon	SCONJ
iajs-898	28	7	an	an	DET
iajs-898	28	8	exogeneous	exogeneous	ADJ
iajs-898	28	9	parameter	parameter	NOUN
iajs-898	28	10	,	,	PUNCT
iajs-898	28	11	say	say	VERB
iajs-898	28	12	k.	k.	PROPN
iajs-898	28	13	for	for	ADP
iajs-898	28	14	any	any	DET
iajs-898	28	15	k	k	PROPN
iajs-898	28	16	the	the	DET
iajs-898	28	17	corresponding	corresponding	PROPN
iajs-898	28	18	ridge	ridge	PROPN
iajs-898	28	19	estimator	estimator	NOUN
iajs-898	28	20	denoted	denote	VERB
iajs-898	28	21	by	by	ADP
iajs-898	28	22	is	be	AUX
iajs-898	28	23	defined	define	VERB
iajs-898	28	24	to	to	PART
iajs-898	28	25	be	be	AUX
iajs-898	28	26	:	:	PUNCT
iajs-898	28	27	=	=	SYM
iajs-898	28	28	x'y	x'y	PROPN
iajs-898	28	29	…	…	PUNCT
iajs-898	28	30	…	…	PUNCT
iajs-898	28	31	.(4	.(4	NUM
iajs-898	28	32	)	)	PUNCT
iajs-898	28	33	(	(	PUNCT
iajs-898	28	34	see[1	see[1	X
iajs-898	28	35	]	]	PUNCT
iajs-898	28	36	)	)	PUNCT
iajs-898	28	37	ihjpas	ihjpa	VERB
iajs-898	28	38	ibn	ibn	PROPN
iajs-898	28	39	alhaitham	alhaitham	PROPN
iajs-898	29	1	j.	j.	PROPN
iajs-898	30	1	fo	fo	ADP
iajs-898	30	2	r	r	NOUN
iajs-898	30	3	pure	pure	ADJ
iajs-898	30	4	&	&	CCONJ
iajs-898	30	5	appl	appl	PROPN
iajs-898	30	6	.	.	PUNCT
iajs-898	31	1	sc	sc	PROPN
iajs-898	31	2	i.	i.	PROPN
iajs-898	31	3	vo	vo	PROPN
iajs-898	31	4	l.23	l.23	PROPN
iajs-898	31	5	(	(	PUNCT
iajs-898	31	6	3	3	NUM
iajs-898	31	7	)	)	PUNCT
iajs-898	31	8	2010	2010	NUM
iajs-898	31	9	we	we	PRON
iajs-898	31	10	argue	argue	VERB
iajs-898	31	11	that	that	SCONJ
iajs-898	31	12	it	it	PRON
iajs-898	31	13	is	be	AUX
iajs-898	31	14	typically	typically	ADV
iajs-898	31	15	true	true	ADJ
iajs-898	31	16	that	that	SCONJ
iajs-898	31	17	there	there	PRON
iajs-898	31	18	is	be	VERB
iajs-898	31	19	available	available	ADJ
iajs-898	31	20	prior	prior	ADJ
iajs-898	31	21	informations	information	NOUN
iajs-898	31	22	about	about	ADP
iajs-898	31	23	the	the	DET
iajs-898	31	24	parameters	parameter	NOUN
iajs-898	31	25	,	,	PUNCT
iajs-898	31	26	and	and	CCONJ
iajs-898	31	27	this	this	PRON
iajs-898	31	28	may	may	AUX
iajs-898	31	29	be	be	AUX
iajs-898	31	30	exploited	exploit	VERB
iajs-898	31	31	to	to	PART
iajs-898	31	32	find	find	VERB
iajs-898	31	33	improved	improved	ADJ
iajs-898	31	34	estimators	estimator	NOUN
iajs-898	31	35	.	.	PUNCT
iajs-898	32	1	in	in	ADP
iajs-898	32	2	this	this	DET
iajs-898	32	3	paper	paper	NOUN
iajs-898	32	4	attention	attention	NOUN
iajs-898	32	5	is	be	AUX
iajs-898	32	6	focuced	focuce	VERB
iajs-898	32	7	upon	upon	SCONJ
iajs-898	32	8	bayesian	bayesian	NOUN
iajs-898	32	9	formulation	formulation	NOUN
iajs-898	32	10	for	for	ADP
iajs-898	32	11	generalized	generalized	ADJ
iajs-898	32	12	shrinkage	shrinkage	NOUN
iajs-898	32	13	estimators	estimator	NOUN
iajs-898	32	14	and	and	CCONJ
iajs-898	32	15	ordinary	ordinary	ADJ
iajs-898	32	16	ridge	ridge	PROPN
iajs-898	32	17	regression	regression	PROPN
iajs-898	32	18	estimator	estimator	NOUN
iajs-898	32	19	,	,	PUNCT
iajs-898	32	20	moreover	moreover	ADV
iajs-898	32	21	a	a	DET
iajs-898	32	22	bayes	bayes	NOUN
iajs-898	32	23	estimator	estimator	NOUN
iajs-898	32	24	as	as	ADV
iajs-898	32	25	well	well	ADV
iajs-898	32	26	as	as	ADP
iajs-898	32	27	a	a	DET
iajs-898	32	28	conventional	conventional	ADJ
iajs-898	32	29	estimator	estimator	NOUN
iajs-898	32	30	for	for	ADP
iajs-898	32	31	the	the	DET
iajs-898	32	32	ridge	ridge	NOUN
iajs-898	32	33	parameter	parameter	NOUN
iajs-898	32	34	is	be	AUX
iajs-898	32	35	derived	derive	VERB
iajs-898	32	36	.	.	PUNCT
iajs-898	33	1	generalized	generalized	ADJ
iajs-898	33	2	shrinkage	shrinkage	NOUN
iajs-898	33	3	estimators	estimator	NOUN
iajs-898	33	4	:	:	PUNCT
iajs-898	33	5	given	give	VERB
iajs-898	33	6	an	an	DET
iajs-898	33	7	(	(	PUNCT
iajs-898	33	8	n	n	CCONJ
iajs-898	33	9	)	)	PUNCT
iajs-898	33	10	matrix	matrix	NOUN
iajs-898	33	11	of	of	ADP
iajs-898	33	12	regressors	regressor	NOUN
iajs-898	33	13	x	x	PUNCT
iajs-898	33	14	and	and	CCONJ
iajs-898	33	15	an	an	DET
iajs-898	33	16	(	(	PUNCT
iajs-898	33	17	n	n	NOUN
iajs-898	33	18	)	)	PUNCT
iajs-898	33	19	vector	vector	NOUN
iajs-898	33	20	of	of	ADP
iajs-898	33	21	the	the	DET
iajs-898	33	22	corresponding	corresponding	ADJ
iajs-898	33	23	response	response	NOUN
iajs-898	33	24	y.	y.	PROPN
iajs-898	33	25	assume	assume	VERB
iajs-898	33	26	that	that	SCONJ
iajs-898	33	27	sample	sample	NOUN
iajs-898	33	28	means	mean	NOUN
iajs-898	33	29	have	have	AUX
iajs-898	33	30	been	be	AUX
iajs-898	33	31	removed	remove	VERB
iajs-898	33	32	from	from	ADP
iajs-898	33	33	the	the	DET
iajs-898	33	34	data	datum	NOUN
iajs-898	33	35	(	(	PUNCT
iajs-898	33	36	so	so	SCONJ
iajs-898	33	37	that	that	DET
iajs-898	33	38	1'x=0'and	1'x=0'and	NUM
iajs-898	33	39	1'y=0	1'y=0	NUM
iajs-898	33	40	,	,	PUNCT
iajs-898	33	41	where	where	SCONJ
iajs-898	33	42	1	1	NUM
iajs-898	33	43	is	be	AUX
iajs-898	33	44	an	an	DET
iajs-898	33	45	n_vector	n_vector	NOUN
iajs-898	33	46	of	of	ADP
iajs-898	33	47	ones	one	NOUN
iajs-898	33	48	.	.	PUNCT
iajs-898	33	49	)	)	PUNCT
iajs-898	33	50	and	and	CCONJ
iajs-898	33	51	write	write	VERB
iajs-898	33	52	the	the	DET
iajs-898	33	53	standard	standard	ADJ
iajs-898	33	54	linear	linear	ADJ
iajs-898	33	55	regression	regression	NOUN
iajs-898	33	56	model	model	NOUN
iajs-898	33	57	as	as	ADP
iajs-898	33	58	e(y|x)=xβ	e(y|x)=xβ	ADJ
iajs-898	33	59	and	and	CCONJ
iajs-898	33	60	var(y|x)=	var(y|x)=	NUM
iajs-898	33	61	(	(	PUNCT
iajs-898	33	62	i-11'/n	i-11'/n	INTJ
iajs-898	33	63	)	)	PUNCT
iajs-898	33	64	where	where	SCONJ
iajs-898	33	65	β	β	PROPN
iajs-898	33	66	is	be	AUX
iajs-898	33	67	a	a	DET
iajs-898	33	68	vector	vector	NOUN
iajs-898	33	69	of	of	ADP
iajs-898	33	70	unknown	unknown	ADJ
iajs-898	33	71	regression	regression	NOUN
iajs-898	33	72	coefficients	coefficient	NOUN
iajs-898	33	73	and	and	CCONJ
iajs-898	33	74	is	be	AUX
iajs-898	33	75	the	the	DET
iajs-898	33	76	unknown	unknown	ADJ
iajs-898	33	77	error	error	NOUN
iajs-898	33	78	variance	variance	NOUN
iajs-898	33	79	.	.	PUNCT
iajs-898	34	1	the	the	DET
iajs-898	34	2	singular	singular	ADJ
iajs-898	34	3	value	value	NOUN
iajs-898	34	4	decomposition	decomposition	NOUN
iajs-898	34	5	of	of	ADP
iajs-898	34	6	x	x	PUNCT
iajs-898	34	7	will	will	AUX
iajs-898	34	8	be	be	AUX
iajs-898	34	9	denoted	denote	VERB
iajs-898	34	10	by	by	ADP
iajs-898	34	11	:	:	PUNCT
iajs-898	34	12	(	(	PUNCT
iajs-898	34	13	see[3	see[3	X
iajs-898	34	14	]	]	PUNCT
iajs-898	34	15	)	)	PUNCT
iajs-898	34	16	.	.	PUNCT
iajs-898	35	1	x	x	X
iajs-898	35	2	=	=	NOUN
iajs-898	35	3	h	h	NOUN
iajs-898	35	4	g	g	NOUN
iajs-898	35	5	'	'	PUNCT
iajs-898	35	6	…	…	PUNCT
iajs-898	35	7	…	…	SYM
iajs-898	35	8	.(5	.(5	NOUN
iajs-898	35	9	)	)	PUNCT
iajs-898	35	10	where	where	SCONJ
iajs-898	35	11	h	h	NOUN
iajs-898	35	12	is	be	AUX
iajs-898	35	13	an	an	DET
iajs-898	35	14	(	(	PUNCT
iajs-898	35	15	semi	semi	ADJ
iajs-898	35	16	orthogonal	orthogonal	ADJ
iajs-898	35	17	matrix	matrix	NOUN
iajs-898	35	18	satisfy	satisfy	NOUN
iajs-898	35	19	h'h=	h'h=	PROPN
iajs-898	35	20	,	,	PUNCT
iajs-898	35	21	is	be	AUX
iajs-898	35	22	a	a	DET
iajs-898	35	23	(	(	PUNCT
iajs-898	35	24	p	p	NOUN
iajs-898	35	25	diagonal	diagonal	ADJ
iajs-898	35	26	matrix	matrix	NOUN
iajs-898	35	27	of	of	ADP
iajs-898	35	28	orderd	orderd	ADJ
iajs-898	35	29	singular	singular	ADJ
iajs-898	35	30	values	value	NOUN
iajs-898	35	31	of	of	ADP
iajs-898	35	32	x	x	X
iajs-898	35	33	,	,	PUNCT
iajs-898	35	34	…	…	PUNCT
iajs-898	35	35	..	..	PUNCT
iajs-898	35	36	>	>	X
iajs-898	35	37	0	0	NUM
iajs-898	35	38	,	,	PUNCT
iajs-898	35	39	g	g	PROPN
iajs-898	35	40	is	be	AUX
iajs-898	35	41	a	a	DET
iajs-898	35	42	(	(	PUNCT
iajs-898	35	43	p	p	NOUN
iajs-898	35	44	orthogonal	orthogonal	ADJ
iajs-898	35	45	matrix	matrix	NOUN
iajs-898	35	46	whose	whose	DET
iajs-898	35	47	columns	column	NOUN
iajs-898	35	48	represent	represent	VERB
iajs-898	35	49	the	the	DET
iajs-898	35	50	eigenvectors	eigenvector	NOUN
iajs-898	35	51	of	of	ADP
iajs-898	35	52	the	the	DET
iajs-898	35	53	information	information	NOUN
iajs-898	35	54	matrix	matrix	NOUN
iajs-898	35	55	x'x	x'x	PROPN
iajs-898	35	56	.	.	PUNCT
iajs-898	36	1	assume	assume	VERB
iajs-898	36	2	that	that	SCONJ
iajs-898	36	3	>	>	X
iajs-898	36	4	0	0	PUNCT
iajs-898	37	1	so	so	SCONJ
iajs-898	37	2	that	that	SCONJ
iajs-898	37	3	β	β	NOUN
iajs-898	37	4	is	be	AUX
iajs-898	37	5	estimable	estimable	ADJ
iajs-898	37	6	,	,	PUNCT
iajs-898	37	7	then	then	ADV
iajs-898	37	8	as	as	ADP
iajs-898	37	9	in	in	ADP
iajs-898	37	10	"	"	PUNCT
iajs-898	37	11	obenchain	obenchain	NOUN
iajs-898	37	12	(	(	PUNCT
iajs-898	37	13	1978	1978	NUM
iajs-898	37	14	)	)	PUNCT
iajs-898	37	15	"	"	PUNCT
iajs-898	37	16	(	(	PUNCT
iajs-898	37	17	see	see	VERB
iajs-898	37	18	[	[	X
iajs-898	37	19	4	4	NUM
iajs-898	37	20	]	]	PUNCT
iajs-898	37	21	)	)	PUNCT
iajs-898	37	22	we	we	PRON
iajs-898	37	23	get	get	VERB
iajs-898	37	24	=	=	PUNCT
iajs-898	37	25	gc	gc	PROPN
iajs-898	37	26	where	where	SCONJ
iajs-898	37	27	c=	c=	NOUN
iajs-898	37	28	h'y	h'y	PRON
iajs-898	37	29	contains	contain	VERB
iajs-898	37	30	the	the	DET
iajs-898	37	31	uncorrelated	uncorrelated	ADJ
iajs-898	37	32	components	component	NOUN
iajs-898	37	33	of	of	ADP
iajs-898	37	34	where	where	SCONJ
iajs-898	37	35	e(c)=e(g	e(c)=e(g	NOUN
iajs-898	37	36	'	'	PUNCT
iajs-898	37	37	)	)	PUNCT
iajs-898	38	1	=	=	PUNCT
iajs-898	38	2	g'β	g'β	NOUN
iajs-898	38	3	=	=	NOUN
iajs-898	38	4	γ	γ	NOUN
iajs-898	38	5	say	say	VERB
iajs-898	38	6	,	,	PUNCT
iajs-898	38	7	and	and	CCONJ
iajs-898	38	8	:	:	PUNCT
iajs-898	38	9	var(c)=var(g	var(c)=var(g	NOUN
iajs-898	38	10	'	'	PUNCT
iajs-898	38	11	)	)	PUNCT
iajs-898	39	1	=	=	SYM
iajs-898	39	2	g'var	g'var	PROPN
iajs-898	39	3	(	(	PUNCT
iajs-898	39	4	)	)	PUNCT
iajs-898	39	5	g=	g=	NOUN
iajs-898	39	6	g	g	NOUN
iajs-898	39	7	'	'	PUNCT
iajs-898	39	8	g=	g=	NOUN
iajs-898	39	9	.	.	PUNCT
iajs-898	40	1	notice	notice	VERB
iajs-898	40	2	that	that	SCONJ
iajs-898	40	3	the	the	DET
iajs-898	40	4	elements	element	NOUN
iajs-898	40	5	of	of	ADP
iajs-898	40	6	c	c	PROPN
iajs-898	40	7	are	be	AUX
iajs-898	40	8	uncorrelated	uncorrelated	ADJ
iajs-898	40	9	since	since	SCONJ
iajs-898	40	10	their	their	PRON
iajs-898	40	11	variance	variance	NOUN
iajs-898	40	12	matrix	matrix	NOUN
iajs-898	40	13	is	be	AUX
iajs-898	40	14	diagonal	diagonal	ADJ
iajs-898	40	15	.	.	PUNCT
iajs-898	41	1	the	the	DET
iajs-898	41	2	vector	vector	NOUN
iajs-898	41	3	of	of	ADP
iajs-898	41	4	generalized	generalized	ADJ
iajs-898	41	5	shrinkage	shrinkage	NOUN
iajs-898	41	6	estimator	estimator	NOUN
iajs-898	41	7	(	(	PUNCT
iajs-898	41	8	or	or	CCONJ
iajs-898	41	9	generalized	generalized	ADJ
iajs-898	41	10	ridge	ridge	PROPN
iajs-898	41	11	regression	regression	PROPN
iajs-898	41	12	estimator	estimator	NOUN
iajs-898	41	13	)	)	PUNCT
iajs-898	41	14	will	will	AUX
iajs-898	41	15	be	be	AUX
iajs-898	41	16	denoted	denote	VERB
iajs-898	41	17	here	here	ADV
iajs-898	41	18	by	by	ADV
iajs-898	41	19	and	and	CCONJ
iajs-898	41	20	will	will	AUX
iajs-898	41	21	be	be	AUX
iajs-898	41	22	of	of	ADP
iajs-898	41	23	the	the	DET
iajs-898	41	24	general	general	ADJ
iajs-898	41	25	form	form	NOUN
iajs-898	41	26	:	:	PUNCT
iajs-898	41	27	(	(	PUNCT
iajs-898	41	28	see[4	see[4	X
iajs-898	41	29	]	]	X
iajs-898	41	30	)	)	PUNCT
iajs-898	42	1	=	=	SYM
iajs-898	42	2	g∆c=	g∆c=	NOUN
iajs-898	42	3	…	…	PUNCT
iajs-898	42	4	…	…	PUNCT
iajs-898	42	5	..	..	PUNCT
iajs-898	42	6	(	(	PUNCT
iajs-898	42	7	6	6	NUM
iajs-898	42	8	)	)	PUNCT
iajs-898	42	9	where	where	SCONJ
iajs-898	42	10	is	be	AUX
iajs-898	42	11	the	the	DET
iajs-898	42	12	j_th	j_th	ADJ
iajs-898	42	13	column	column	NOUN
iajs-898	42	14	of	of	ADP
iajs-898	42	15	the	the	DET
iajs-898	42	16	matrix	matrix	NOUN
iajs-898	42	17	g	g	NOUN
iajs-898	42	18	,	,	PUNCT
iajs-898	42	19	is	be	AUX
iajs-898	42	20	the	the	DET
iajs-898	42	21	j_th	j_th	PROPN
iajs-898	42	22	diagonal	diagonal	ADJ
iajs-898	42	23	element	element	NOUN
iajs-898	42	24	of	of	ADP
iajs-898	42	25	the	the	DET
iajs-898	42	26	shrinkage	shrinkage	NOUN
iajs-898	42	27	factors	factor	NOUN
iajs-898	42	28	matrix	matrix	NOUN
iajs-898	42	29	∆	∆	PROPN
iajs-898	42	30	,	,	PUNCT
iajs-898	42	31	we	we	PRON
iajs-898	42	32	will	will	AUX
iajs-898	42	33	usually	usually	ADV
iajs-898	42	34	restrict	restrict	VERB
iajs-898	42	35	the	the	DET
iajs-898	42	36	range	range	NOUN
iajs-898	42	37	of	of	ADP
iajs-898	42	38	shrinkage	shrinkage	NOUN
iajs-898	42	39	factors	factor	NOUN
iajs-898	42	40	to	to	ADP
iajs-898	42	41	0	0	NUM
iajs-898	42	42	,	,	PUNCT
iajs-898	42	43	j=	j=	ADP
iajs-898	42	44	1,2	1,2	NUM
iajs-898	42	45	,	,	PUNCT
iajs-898	42	46	…	…	PUNCT
iajs-898	42	47	.,p	.,p	X
iajs-898	42	48	.	.	PROPN
iajs-898	42	49	is	be	AUX
iajs-898	42	50	the	the	DET
iajs-898	42	51	j_th	j_th	PROPN
iajs-898	42	52	element	element	NOUN
iajs-898	42	53	of	of	ADP
iajs-898	42	54	the	the	DET
iajs-898	42	55	uncorrelated	uncorrelated	ADJ
iajs-898	42	56	components	component	NOUN
iajs-898	42	57	vector	vector	PROPN
iajs-898	42	58	c.	c.	PROPN
iajs-898	42	59	in	in	ADP
iajs-898	42	60	the	the	DET
iajs-898	42	61	remaining	remain	VERB
iajs-898	42	62	of	of	ADP
iajs-898	42	63	this	this	DET
iajs-898	42	64	section	section	NOUN
iajs-898	42	65	we	we	PRON
iajs-898	42	66	discuss	discuss	VERB
iajs-898	42	67	bayesian	bayesian	NOUN
iajs-898	42	68	methods	method	NOUN
iajs-898	42	69	for	for	ADP
iajs-898	42	70	defining	define	VERB
iajs-898	42	71	the	the	DET
iajs-898	42	72	form	form	NOUN
iajs-898	42	73	of	of	ADP
iajs-898	42	74	shrinkage	shrinkage	NOUN
iajs-898	42	75	of	of	ADP
iajs-898	42	76	sample	sample	NOUN
iajs-898	42	77	estimates	estimate	NOUN
iajs-898	42	78	towards	towards	ADP
iajs-898	42	79	a	a	DET
iajs-898	42	80	subjective	subjective	ADJ
iajs-898	42	81	prior	prior	ADV
iajs-898	42	82	distribution.lindely	distribution.lindely	ADV
iajs-898	42	83	and	and	CCONJ
iajs-898	42	84	smith	smith	PROPN
iajs-898	42	85	(	(	PUNCT
iajs-898	42	86	1972	1972	NUM
iajs-898	42	87	)	)	PUNCT
iajs-898	42	88	describe	describe	VERB
iajs-898	42	89	a	a	DET
iajs-898	42	90	bayesian	bayesian	NOUN
iajs-898	42	91	formalizim	formalizim	NOUN
iajs-898	42	92	for	for	ADP
iajs-898	42	93	hierarchical	hierarchical	ADJ
iajs-898	42	94	(	(	PUNCT
iajs-898	42	95	multi_stage	multi_stage	NOUN
iajs-898	42	96	)	)	PUNCT
iajs-898	42	97	analysis	analysis	NOUN
iajs-898	42	98	of	of	ADP
iajs-898	42	99	linear	linear	NOUN
iajs-898	42	100	models	model	NOUN
iajs-898	42	101	using	use	VERB
iajs-898	42	102	conjugate	conjugate	ADJ
iajs-898	42	103	ihjpas	ihjpa	NOUN
iajs-898	42	104	ibn	ibn	PROPN
iajs-898	42	105	alhaitham	alhaitham	PROPN
iajs-898	43	1	j.	j.	PROPN
iajs-898	44	1	fo	fo	ADP
iajs-898	44	2	r	r	NOUN
iajs-898	44	3	pure	pure	ADJ
iajs-898	44	4	&	&	CCONJ
iajs-898	44	5	appl	appl	PROPN
iajs-898	44	6	.	.	PUNCT
iajs-898	45	1	sc	sc	PROPN
iajs-898	45	2	i.	i.	PROPN
iajs-898	45	3	vo	vo	PROPN
iajs-898	45	4	l.23	l.23	PROPN
iajs-898	45	5	(	(	PUNCT
iajs-898	45	6	3	3	NUM
iajs-898	45	7	)	)	PUNCT
iajs-898	45	8	2010	2010	NUM
iajs-898	45	9	multivariate_normal	multivariate_normal	PROPN
iajs-898	45	10	prior	prior	ADV
iajs-898	45	11	distribution.(see	distribution.(see	NOUN
iajs-898	46	1	[	[	X
iajs-898	46	2	5	5	NUM
iajs-898	46	3	]	]	PUNCT
iajs-898	46	4	)	)	PUNCT
iajs-898	46	5	this	this	DET
iajs-898	46	6	formalism	formalism	NOUN
iajs-898	46	7	expresses	express	VERB
iajs-898	46	8	unknown	unknown	ADJ
iajs-898	46	9	parameters	parameter	NOUN
iajs-898	46	10	at	at	ADP
iajs-898	46	11	each	each	DET
iajs-898	46	12	stage	stage	NOUN
iajs-898	46	13	of	of	ADP
iajs-898	46	14	an	an	DET
iajs-898	46	15	analysis	analysis	NOUN
iajs-898	46	16	in	in	ADP
iajs-898	46	17	terms	term	NOUN
iajs-898	46	18	of	of	ADP
iajs-898	46	19	a	a	DET
iajs-898	46	20	linear	linear	ADJ
iajs-898	46	21	model	model	NOUN
iajs-898	46	22	at	at	ADP
iajs-898	46	23	the	the	DET
iajs-898	46	24	previous	previous	ADJ
iajs-898	46	25	lower	low	ADJ
iajs-898	46	26	stage	stage	NOUN
iajs-898	46	27	.	.	PUNCT
iajs-898	47	1	but	but	CCONJ
iajs-898	47	2	,	,	PUNCT
iajs-898	47	3	although	although	SCONJ
iajs-898	47	4	dispersion	dispersion	NOUN
iajs-898	47	5	matrices	matrix	NOUN
iajs-898	47	6	at	at	ADP
iajs-898	47	7	each	each	DET
iajs-898	47	8	stage	stage	NOUN
iajs-898	47	9	can	can	AUX
iajs-898	47	10	be	be	AUX
iajs-898	47	11	arbitrary	arbitrary	ADJ
iajs-898	47	12	,	,	PUNCT
iajs-898	47	13	they	they	PRON
iajs-898	47	14	must	must	AUX
iajs-898	47	15	be	be	AUX
iajs-898	47	16	known	know	VERB
iajs-898	47	17	.	.	PUNCT
iajs-898	48	1	and	and	CCONJ
iajs-898	48	2	,	,	PUNCT
iajs-898	48	3	at	at	ADP
iajs-898	48	4	the	the	DET
iajs-898	48	5	final	final	ADJ
iajs-898	48	6	stage	stage	NOUN
iajs-898	48	7	,	,	PUNCT
iajs-898	48	8	both	both	CCONJ
iajs-898	48	9	the	the	DET
iajs-898	48	10	mean	mean	ADJ
iajs-898	48	11	vector	vector	NOUN
iajs-898	48	12	and	and	CCONJ
iajs-898	48	13	the	the	DET
iajs-898	48	14	dispersion	dispersion	NOUN
iajs-898	48	15	matrix	matrix	NOUN
iajs-898	48	16	must	must	AUX
iajs-898	48	17	be	be	AUX
iajs-898	48	18	known.the	known.the	DET
iajs-898	48	19	fundamental	fundamental	ADJ
iajs-898	48	20	lemma	lemma	PROPN
iajs-898	48	21	of	of	ADP
iajs-898	48	22	lindely	lindely	ADV
iajs-898	48	23	and	and	CCONJ
iajs-898	48	24	smith	smith	PROPN
iajs-898	48	25	(	(	PUNCT
iajs-898	48	26	1972	1972	NUM
iajs-898	48	27	)	)	PUNCT
iajs-898	48	28	states	state	VERB
iajs-898	48	29	that	that	SCONJ
iajs-898	48	30	:	:	PUNCT
iajs-898	48	31	lemma	lemma	PROPN
iajs-898	48	32	:	:	PUNCT
iajs-898	48	33	if	if	SCONJ
iajs-898	48	34	the	the	DET
iajs-898	48	35	sampeling	sampele	VERB
iajs-898	48	36	distribution	distribution	NOUN
iajs-898	48	37	of	of	ADP
iajs-898	48	38	the	the	DET
iajs-898	48	39	response	response	NOUN
iajs-898	48	40	,	,	PUNCT
iajs-898	48	41	y	y	PROPN
iajs-898	48	42	,	,	PUNCT
iajs-898	48	43	is	be	AUX
iajs-898	48	44	y|	y|	NOUN
iajs-898	48	45	~	~	PUNCT
iajs-898	48	46	n	n	CCONJ
iajs-898	48	47	(	(	PUNCT
iajs-898	48	48	,	,	PUNCT
iajs-898	48	49	)	)	PUNCT
iajs-898	48	50	where	where	SCONJ
iajs-898	48	51	is	be	AUX
iajs-898	48	52	(	(	PUNCT
iajs-898	48	53	×1	×1	NOUN
iajs-898	48	54	)	)	PUNCT
iajs-898	48	55	parameter	parameter	NOUN
iajs-898	48	56	vector	vector	NOUN
iajs-898	48	57	and	and	CCONJ
iajs-898	48	58	the	the	DET
iajs-898	48	59	prior	prior	ADJ
iajs-898	48	60	distribution	distribution	NOUN
iajs-898	48	61	is	be	AUX
iajs-898	48	62	~	~	PUNCT
iajs-898	48	63	n	n	CCONJ
iajs-898	48	64	(	(	PUNCT
iajs-898	48	65	,	,	PUNCT
iajs-898	48	66	)	)	PUNCT
iajs-898	48	67	where	where	SCONJ
iajs-898	48	68	is	be	AUX
iajs-898	48	69	(	(	PUNCT
iajs-898	48	70	×1	×1	NOUN
iajs-898	48	71	)	)	PUNCT
iajs-898	48	72	parameter	parameter	NOUN
iajs-898	48	73	vector	vector	NOUN
iajs-898	48	74	,	,	PUNCT
iajs-898	48	75	then	then	ADV
iajs-898	48	76	the	the	DET
iajs-898	48	77	marginal	marginal	ADJ
iajs-898	48	78	(	(	PUNCT
iajs-898	48	79	unconditional	unconditional	ADJ
iajs-898	48	80	)	)	PUNCT
iajs-898	48	81	distribution	distribution	NOUN
iajs-898	48	82	of	of	ADP
iajs-898	48	83	y	y	PROPN
iajs-898	48	84	is	be	AUX
iajs-898	48	85	:	:	PUNCT
iajs-898	48	86	y	y	PROPN
iajs-898	48	87	~	~	PUNCT
iajs-898	48	88	n	n	CCONJ
iajs-898	48	89	(	(	PUNCT
iajs-898	48	90	,	,	PUNCT
iajs-898	48	91	+	+	NUM
iajs-898	48	92	'	'	NUM
iajs-898	48	93	)	)	PUNCT
iajs-898	48	94	…	…	PUNCT
iajs-898	48	95	…	…	PUNCT
iajs-898	48	96	(	(	PUNCT
iajs-898	48	97	7	7	NUM
iajs-898	48	98	)	)	PUNCT
iajs-898	48	99	and	and	CCONJ
iajs-898	48	100	the	the	DET
iajs-898	48	101	posterior	posterior	ADJ
iajs-898	48	102	(	(	PUNCT
iajs-898	48	103	conditional	conditional	ADJ
iajs-898	48	104	)	)	PUNCT
iajs-898	48	105	distribution	distribution	NOUN
iajs-898	48	106	of	of	ADP
iajs-898	48	107	given	give	VERB
iajs-898	48	108	y	y	PROPN
iajs-898	48	109	is	be	AUX
iajs-898	48	110	:	:	PUNCT
iajs-898	48	111	|y	|y	X
iajs-898	48	112	~	~	PUNCT
iajs-898	48	113	n(bb	n(bb	X
iajs-898	48	114	,	,	PUNCT
iajs-898	48	115	b	b	NOUN
iajs-898	48	116	)	)	PUNCT
iajs-898	48	117	…	…	PUNCT
iajs-898	48	118	…	…	PUNCT
iajs-898	48	119	…	…	PUNCT
iajs-898	48	120	(	(	PUNCT
iajs-898	48	121	8)	8)	NUM
iajs-898	48	122	where	where	SCONJ
iajs-898	48	123	:	:	PUNCT
iajs-898	48	124	=	=	PUNCT
iajs-898	48	125	'	'	PUNCT
iajs-898	48	126	+	+	CCONJ
iajs-898	48	127	and	and	CCONJ
iajs-898	48	128	b=	b=	NOUN
iajs-898	48	129	'	'	PART
iajs-898	48	130	y+	y+	NOUN
iajs-898	48	131	to	to	PART
iajs-898	48	132	apply	apply	VERB
iajs-898	48	133	this	this	DET
iajs-898	48	134	lemma	lemma	PROPN
iajs-898	48	135	and	and	CCONJ
iajs-898	48	136	demonstrate	demonstrate	VERB
iajs-898	48	137	that	that	SCONJ
iajs-898	48	138	a	a	DET
iajs-898	48	139	simple	simple	ADJ
iajs-898	48	140	2_stage	2_stage	NUM
iajs-898	48	141	bayesian	bayesian	NOUN
iajs-898	48	142	formalism	formalism	NOUN
iajs-898	48	143	produces	produce	VERB
iajs-898	48	144	generalized	generalized	ADJ
iajs-898	48	145	shrinkage	shrinkage	NOUN
iajs-898	48	146	estimators	estimator	NOUN
iajs-898	48	147	,	,	PUNCT
iajs-898	48	148	we	we	PRON
iajs-898	48	149	first	first	ADV
iajs-898	48	150	make	make	VERB
iajs-898	48	151	the	the	DET
iajs-898	48	152	identification	identification	NOUN
iajs-898	48	153	:	:	PUNCT
iajs-898	49	1	=	=	PUNCT
iajs-898	49	2	xβ	xβ	ADP
iajs-898	49	3	,	,	PUNCT
iajs-898	49	4	=	=	PUNCT
iajs-898	50	1	i	i	PRON
iajs-898	50	2	and	and	CCONJ
iajs-898	50	3	=	=	SYM
iajs-898	50	4	x'x	x'x	PROPN
iajs-898	50	5	+	+	X
iajs-898	50	6	.	.	PUNCT
iajs-898	51	1	next	next	ADV
iajs-898	51	2	,	,	PUNCT
iajs-898	51	3	we	we	PRON
iajs-898	51	4	set	set	VERB
iajs-898	51	5	the	the	DET
iajs-898	51	6	prior	prior	ADJ
iajs-898	51	7	mean	mean	ADJ
iajs-898	51	8	value	value	NOUN
iajs-898	51	9	for	for	ADP
iajs-898	51	10	β	β	NOUN
iajs-898	51	11	to	to	ADP
iajs-898	51	12	zero	zero	NUM
iajs-898	51	13	by	by	ADP
iajs-898	51	14	taking	take	VERB
iajs-898	51	15	=	=	NOUN
iajs-898	51	16	0	0	NUM
iajs-898	51	17	and	and	CCONJ
iajs-898	51	18	assume	assume	VERB
iajs-898	51	19	that	that	SCONJ
iajs-898	51	20	(	(	PUNCT
iajs-898	51	21	and	and	CCONJ
iajs-898	51	22	)	)	PUNCT
iajs-898	51	23	will	will	AUX
iajs-898	51	24	be	be	AUX
iajs-898	51	25	simultaneously	simultaneously	ADV
iajs-898	51	26	diagonalizable	diagonalizable	ADJ
iajs-898	51	27	with	with	ADP
iajs-898	51	28	x'x	x'x	NOUN
iajs-898	51	29	by	by	ADP
iajs-898	51	30	restricting	restrict	VERB
iajs-898	51	31	attention	attention	NOUN
iajs-898	51	32	to	to	ADP
iajs-898	51	33	prior	prior	ADJ
iajs-898	51	34	variance_covariance	variance_covariance	NOUN
iajs-898	51	35	matrices	matrix	NOUN
iajs-898	51	36	of	of	ADP
iajs-898	51	37	the	the	DET
iajs-898	51	38	general	general	ADJ
iajs-898	51	39	form	form	NOUN
iajs-898	51	40	=	=	SYM
iajs-898	51	41	.g	.g	NOUN
iajs-898	51	42	g	g	NOUN
iajs-898	51	43	'	'	PUNCT
iajs-898	51	44	,	,	PUNCT
iajs-898	51	45	where	where	SCONJ
iajs-898	51	46	k	k	PROPN
iajs-898	51	47	is	be	AUX
iajs-898	51	48	a	a	DET
iajs-898	51	49	diagonal	diagonal	ADJ
iajs-898	51	50	p×p	p×p	NOUN
iajs-898	51	51	matrix	matrix	NOUN
iajs-898	51	52	and	and	CCONJ
iajs-898	51	53	g	g	NOUN
iajs-898	51	54	is	be	AUX
iajs-898	51	55	defined	define	VERB
iajs-898	51	56	as	as	ADP
iajs-898	51	57	in	in	ADP
iajs-898	51	58	(	(	PUNCT
iajs-898	51	59	5	5	NUM
iajs-898	51	60	)	)	PUNCT
iajs-898	51	61	.	.	PUNCT
iajs-898	52	1	now	now	ADV
iajs-898	52	2	the	the	DET
iajs-898	52	3	bayes	bayes	PROPN
iajs-898	52	4	estimate	estimate	VERB
iajs-898	52	5	is	be	AUX
iajs-898	52	6	the	the	DET
iajs-898	52	7	mean	mean	ADJ
iajs-898	52	8	bb	bb	NOUN
iajs-898	52	9	of	of	ADP
iajs-898	52	10	the	the	DET
iajs-898	52	11	posterior	posterior	ADJ
iajs-898	52	12	distribution	distribution	NOUN
iajs-898	52	13	of	of	ADP
iajs-898	52	14	β	β	PRON
iajs-898	52	15	given	give	VERB
iajs-898	52	16	y	y	PROPN
iajs-898	52	17	and	and	CCONJ
iajs-898	52	18	this	this	DET
iajs-898	52	19	mean	mean	ADJ
iajs-898	52	20	vector	vector	NOUN
iajs-898	52	21	is	be	AUX
iajs-898	52	22	of	of	ADP
iajs-898	52	23	the	the	DET
iajs-898	52	24	general	general	ADJ
iajs-898	52	25	form	form	NOUN
iajs-898	52	26	:	:	PUNCT
iajs-898	52	27	e(β|y)=bb=	e(β|y)=bb=	PROPN
iajs-898	52	28	(	(	PUNCT
iajs-898	52	29	'	'	PUNCT
iajs-898	52	30	y+	y+	NUM
iajs-898	52	31	)	)	PUNCT
iajs-898	52	32	=	=	PUNCT
iajs-898	53	1	=	=	PUNCT
iajs-898	53	2	=	=	PUNCT
iajs-898	53	3	g	g	PROPN
iajs-898	53	4	h'y	h'y	PROPN
iajs-898	53	5	=	=	PRON
iajs-898	53	6	g	g	NOUN
iajs-898	53	7	h'y	h'y	NOUN
iajs-898	53	8	ihjpas	ihjpa	VERB
iajs-898	53	9	ibn	ibn	PROPN
iajs-898	53	10	alhaitham	alhaitham	NOUN
iajs-898	54	1	j.	j.	PROPN
iajs-898	55	1	fo	fo	ADP
iajs-898	55	2	r	r	NOUN
iajs-898	55	3	pure	pure	ADJ
iajs-898	55	4	&	&	CCONJ
iajs-898	55	5	appl	appl	PROPN
iajs-898	55	6	.	.	PUNCT
iajs-898	56	1	sc	sc	PROPN
iajs-898	56	2	i.	i.	PROPN
iajs-898	56	3	vo	vo	PROPN
iajs-898	56	4	l.23	l.23	PROPN
iajs-898	56	5	(	(	PUNCT
iajs-898	56	6	3	3	NUM
iajs-898	56	7	)	)	PUNCT
iajs-898	56	8	2010	2010	NUM
iajs-898	57	1	=	=	SYM
iajs-898	57	2	g	g	PROPN
iajs-898	57	3	d	d	X
iajs-898	57	4	h'y	h'y	NOUN
iajs-898	57	5	=	=	SYM
iajs-898	57	6	g∆c	g∆c	NOUN
iajs-898	57	7	=	=	SYM
iajs-898	57	8	…	…	PUNCT
iajs-898	57	9	…	…	PUNCT
iajs-898	57	10	.(9	.(9	NOUN
iajs-898	57	11	)	)	PUNCT
iajs-898	58	1	where	where	SCONJ
iajs-898	58	2	∆	∆	VERB
iajs-898	58	3	=	=	PUNCT
iajs-898	58	4	d	d	NOUN
iajs-898	58	5	is	be	AUX
iajs-898	58	6	the	the	DET
iajs-898	58	7	diagonal	diagonal	ADJ
iajs-898	58	8	matrix	matrix	NOUN
iajs-898	58	9	of	of	ADP
iajs-898	58	10	generalized	generalized	ADJ
iajs-898	58	11	shrinkage	shrinkage	NOUN
iajs-898	58	12	factors	factor	NOUN
iajs-898	58	13	and	and	CCONJ
iajs-898	58	14	c	c	NOUN
iajs-898	58	15	is	be	AUX
iajs-898	58	16	the	the	DET
iajs-898	58	17	vector	vector	NOUN
iajs-898	58	18	of	of	ADP
iajs-898	58	19	uncorrelated	uncorrelated	ADJ
iajs-898	58	20	components	component	NOUN
iajs-898	58	21	of	of	ADP
iajs-898	58	22	the	the	DET
iajs-898	58	23	least	least	ADJ
iajs-898	58	24	squares	square	NOUN
iajs-898	58	25	estimator	estimator	NOUN
iajs-898	58	26	.	.	PUNCT
iajs-898	59	1	the	the	DET
iajs-898	59	2	ordinary	ordinary	PROPN
iajs-898	59	3	ridge	ridge	PROPN
iajs-898	59	4	regression	regression	PROPN
iajs-898	59	5	estimator	estimator	NOUN
iajs-898	59	6	:	:	PUNCT
iajs-898	59	7	in	in	ADP
iajs-898	59	8	the	the	DET
iajs-898	59	9	previous	previous	ADJ
iajs-898	59	10	section	section	NOUN
iajs-898	59	11	we	we	PRON
iajs-898	59	12	have	have	AUX
iajs-898	59	13	demonstrated	demonstrate	VERB
iajs-898	59	14	that	that	SCONJ
iajs-898	59	15	all	all	DET
iajs-898	59	16	generalized	generalized	ADJ
iajs-898	59	17	shrinkage	shrinkage	NOUN
iajs-898	59	18	estimators	estimator	NOUN
iajs-898	59	19	are	be	AUX
iajs-898	59	20	2_stage	2_stage	NUM
iajs-898	59	21	bayes	bayes	NOUN
iajs-898	59	22	estimators	estimator	NOUN
iajs-898	59	23	.	.	PUNCT
iajs-898	60	1	this	this	PRON
iajs-898	60	2	include	include	VERB
iajs-898	60	3	of	of	ADP
iajs-898	60	4	course	course	NOUN
iajs-898	60	5	the	the	DET
iajs-898	60	6	case	case	NOUN
iajs-898	60	7	of	of	ADP
iajs-898	60	8	ordinary	ordinary	ADJ
iajs-898	60	9	ridge	ridge	PROPN
iajs-898	60	10	regression	regression	PROPN
iajs-898	60	11	estimator	estimator	NOUN
iajs-898	60	12	p	p	PROPN
iajs-898	60	13	roposed	ropose	VERB
iajs-898	60	14	by	by	ADP
iajs-898	60	15	hoerl	hoerl	NOUN
iajs-898	60	16	and	and	CCONJ
iajs-898	60	17	kennard	kennard	NOUN
iajs-898	60	18	(	(	PUNCT
iajs-898	60	19	1970	1970	NUM
iajs-898	60	20	)	)	PUNCT
iajs-898	60	21	.	.	PUNCT
iajs-898	61	1	to	to	PART
iajs-898	61	2	demonstrate	demonstrate	VERB
iajs-898	61	3	this	this	DET
iajs-898	61	4	fact	fact	NOUN
iajs-898	61	5	let	let	VERB
iajs-898	61	6	us	we	PRON
iajs-898	61	7	assume	assume	VERB
iajs-898	61	8	that	that	SCONJ
iajs-898	61	9	an	an	DET
iajs-898	61	10	orthogonal	orthogonal	ADJ
iajs-898	61	11	matrix	matrix	NOUN
iajs-898	61	12	p	p	NOUN
iajs-898	61	13	is	be	AUX
iajs-898	61	14	given	give	VERB
iajs-898	61	15	such	such	ADJ
iajs-898	61	16	that	that	SCONJ
iajs-898	61	17	p	p	NOUN
iajs-898	61	18	'	'	PUNCT
iajs-898	61	19	p	p	NOUN
iajs-898	61	20	=	=	X
iajs-898	61	21	d	d	X
iajs-898	61	22	=	=	SYM
iajs-898	61	23	diag	diag	PROPN
iajs-898	61	24	(	(	PUNCT
iajs-898	61	25	,	,	PUNCT
iajs-898	61	26	…	…	PUNCT
iajs-898	61	27	..	..	PUNCT
iajs-898	61	28	,	,	PUNCT
iajs-898	61	29	)	)	PUNCT
iajs-898	61	30	,	,	PUNCT
iajs-898	61	31	≥	≥	NUM
iajs-898	61	32	…	…	SYM
iajs-898	61	33	…	…	SYM
iajs-898	61	34	.≥	.≥	X
iajs-898	61	35	>	>	X
iajs-898	61	36	0	0	X
iajs-898	61	37	.	.	PUNCT
iajs-898	61	38	morever	morever	PROPN
iajs-898	61	39	,	,	PUNCT
iajs-898	61	40	suppose	suppose	VERB
iajs-898	61	41	that	that	SCONJ
iajs-898	61	42	z	z	NOUN
iajs-898	62	1	=	=	PUNCT
iajs-898	62	2	p	p	NOUN
iajs-898	62	3	'	'	PUNCT
iajs-898	62	4	=	=	NOUN
iajs-898	62	5	(	(	PUNCT
iajs-898	62	6	,	,	PUNCT
iajs-898	62	7	…	…	PUNCT
iajs-898	62	8	..	..	PUNCT
iajs-898	62	9	,	,	PUNCT
iajs-898	62	10	)	)	PUNCT
iajs-898	62	11	'	'	PUNCT
iajs-898	62	12	and	and	CCONJ
iajs-898	62	13	w	w	NOUN
iajs-898	62	14	=	=	VERB
iajs-898	62	15	p'β	p'β	NOUN
iajs-898	62	16	=	=	SYM
iajs-898	62	17	(	(	PUNCT
iajs-898	62	18	,	,	PUNCT
iajs-898	62	19	…	…	PUNCT
iajs-898	62	20	.	.	NUM
iajs-898	62	21	,	,	PUNCT
iajs-898	62	22	)	)	PUNCT
iajs-898	62	23	'	'	PUNCT
iajs-898	62	24	then	then	ADV
iajs-898	62	25	:	:	PUNCT
iajs-898	62	26	z	z	X
iajs-898	62	27	~	~	PUNCT
iajs-898	62	28	(	(	PUNCT
iajs-898	62	29	w	w	NOUN
iajs-898	62	30	,	,	PUNCT
iajs-898	62	31	d	d	NOUN
iajs-898	62	32	)	)	PUNCT
iajs-898	62	33	.	.	PUNCT
iajs-898	63	1	let	let	VERB
iajs-898	63	2	us	we	PRON
iajs-898	63	3	assume	assume	VERB
iajs-898	63	4	that	that	SCONJ
iajs-898	63	5	w	w	NOUN
iajs-898	63	6	has	have	VERB
iajs-898	63	7	a	a	DET
iajs-898	63	8	prior	prior	ADJ
iajs-898	63	9	distribution	distribution	NOUN
iajs-898	63	10	given	give	VERB
iajs-898	63	11	by	by	ADP
iajs-898	63	12	w	w	PROPN
iajs-898	63	13	~	~	PUNCT
iajs-898	63	14	(	(	PUNCT
iajs-898	63	15	0	0	NUM
iajs-898	63	16	,	,	PUNCT
iajs-898	63	17	λi	λi	NOUN
iajs-898	63	18	)	)	PUNCT
iajs-898	63	19	for	for	ADP
iajs-898	63	20	some	some	DET
iajs-898	63	21	positive	positive	ADJ
iajs-898	63	22	constant	constant	ADJ
iajs-898	63	23	λ	λ	NOUN
iajs-898	63	24	,	,	PUNCT
iajs-898	63	25	thus	thus	ADV
iajs-898	63	26	,	,	PUNCT
iajs-898	63	27	according	accord	VERB
iajs-898	63	28	to	to	ADP
iajs-898	63	29	the	the	DET
iajs-898	63	30	lemma	lemma	PROPN
iajs-898	63	31	stated	state	VERB
iajs-898	63	32	in	in	ADP
iajs-898	63	33	section	section	NOUN
iajs-898	63	34	2	2	NUM
iajs-898	63	35	,	,	PUNCT
iajs-898	63	36	the	the	DET
iajs-898	63	37	posterior	posterior	ADJ
iajs-898	63	38	distribution	distribution	NOUN
iajs-898	63	39	of	of	ADP
iajs-898	63	40	w	w	NOUN
iajs-898	63	41	given	give	VERB
iajs-898	63	42	z	z	NOUN
iajs-898	63	43	is	be	AUX
iajs-898	63	44	:	:	PUNCT
iajs-898	63	45	w|z	w|z	X
iajs-898	64	1	~	~	PUNCT
iajs-898	65	1	[	[	PUNCT
iajs-898	65	2	λ	λ	X
iajs-898	65	3	z	z	PROPN
iajs-898	65	4	,	,	PUNCT
iajs-898	65	5	λi	λi	X
iajs-898	65	6	]	]	X
iajs-898	65	7	…	…	PUNCT
iajs-898	65	8	…	…	PUNCT
iajs-898	65	9	.(10	.(10	NUM
iajs-898	65	10	)	)	PUNCT
iajs-898	65	11	then	then	ADV
iajs-898	65	12	the	the	DET
iajs-898	65	13	bayes	bayes	PROPN
iajs-898	65	14	estimator	estimator	NOUN
iajs-898	65	15	of	of	ADP
iajs-898	65	16	w	w	PROPN
iajs-898	65	17	is	be	AUX
iajs-898	65	18	:	:	PUNCT
iajs-898	65	19	=	=	PUNCT
iajs-898	65	20	z	z	NOUN
iajs-898	65	21	for	for	ADP
iajs-898	65	22	z	z	PROPN
iajs-898	65	23	=	=	PROPN
iajs-898	65	24	p	p	NOUN
iajs-898	65	25	'	'	PUNCT
iajs-898	65	26	.	.	PUNCT
iajs-898	66	1	consequently	consequently	ADV
iajs-898	66	2	,	,	PUNCT
iajs-898	66	3	the	the	DET
iajs-898	66	4	bayes	bayes	PROPN
iajs-898	66	5	estimator	estimator	NOUN
iajs-898	66	6	of	of	ADP
iajs-898	66	7	β	β	PROPN
iajs-898	66	8	is	be	AUX
iajs-898	66	9	:	:	PUNCT
iajs-898	66	10	p	p	X
iajs-898	66	11	=	=	PUNCT
iajs-898	66	12	p	p	X
iajs-898	66	13	p	p	X
iajs-898	66	14	'	'	PUNCT
iajs-898	66	15	=	=	SYM
iajs-898	66	16	=	=	SYM
iajs-898	66	17	=	=	SYM
iajs-898	66	18	x'y	x'y	PROPN
iajs-898	66	19	for	for	ADP
iajs-898	66	20	k	k	X
iajs-898	66	21	=	=	PUNCT
iajs-898	66	22	=	=	PUNCT
iajs-898	66	23	x'y	x'y	PROPN
iajs-898	66	24	=	=	SYM
iajs-898	66	25	…	…	PUNCT
iajs-898	66	26	…	…	PUNCT
iajs-898	66	27	(	(	PUNCT
iajs-898	66	28	11	11	NUM
iajs-898	66	29	)	)	PUNCT
iajs-898	66	30	.	.	PUNCT
iajs-898	67	1	it	it	PRON
iajs-898	67	2	is	be	AUX
iajs-898	67	3	obvious	obvious	ADJ
iajs-898	67	4	that	that	SCONJ
iajs-898	67	5	the	the	DET
iajs-898	67	6	estimator	estimator	NOUN
iajs-898	67	7	in	in	ADP
iajs-898	67	8	(	(	PUNCT
iajs-898	67	9	11	11	NUM
iajs-898	67	10	)	)	PUNCT
iajs-898	67	11	coincide	coincide	NOUN
iajs-898	67	12	with	with	ADP
iajs-898	67	13	the	the	DET
iajs-898	67	14	ordinary	ordinary	ADJ
iajs-898	67	15	ridge	ridge	PROPN
iajs-898	67	16	regression	regression	PROPN
iajs-898	67	17	estimator	estimator	NOUN
iajs-898	67	18	given	give	VERB
iajs-898	67	19	in	in	ADP
iajs-898	67	20	(	(	PUNCT
iajs-898	67	21	4	4	NUM
iajs-898	67	22	)	)	PUNCT
iajs-898	67	23	.	.	PUNCT
iajs-898	68	1	estimating	estimate	VERB
iajs-898	68	2	the	the	DET
iajs-898	68	3	ridge	ridge	NOUN
iajs-898	68	4	parameter_bayesian	parameter_bayesian	PROPN
iajs-898	68	5	approach	approach	NOUN
iajs-898	68	6	:	:	PUNCT
iajs-898	68	7	there	there	PRON
iajs-898	68	8	are	be	VERB
iajs-898	68	9	many	many	ADJ
iajs-898	68	10	different	different	ADJ
iajs-898	68	11	methods	method	NOUN
iajs-898	68	12	for	for	ADP
iajs-898	68	13	selecting	select	VERB
iajs-898	68	14	the	the	DET
iajs-898	68	15	value	value	NOUN
iajs-898	68	16	of	of	ADP
iajs-898	68	17	the	the	DET
iajs-898	68	18	ridge	ridge	PROPN
iajs-898	68	19	parameter	parameter	PROPN
iajs-898	68	20	k.	k.	PROPN
iajs-898	69	1	the	the	DET
iajs-898	69	2	method	method	NOUN
iajs-898	69	3	we	we	PRON
iajs-898	69	4	try	try	VERB
iajs-898	69	5	here	here	ADV
iajs-898	69	6	is	be	AUX
iajs-898	69	7	based	base	VERB
iajs-898	69	8	upon	upon	SCONJ
iajs-898	69	9	the	the	DET
iajs-898	69	10	lemma	lemma	PROPN
iajs-898	69	11	stated	state	VERB
iajs-898	69	12	in	in	ADP
iajs-898	69	13	section	section	NOUN
iajs-898	69	14	(	(	PUNCT
iajs-898	69	15	2	2	NUM
iajs-898	69	16	)	)	PUNCT
iajs-898	69	17	.	.	PUNCT
iajs-898	70	1	accordingly	accordingly	ADV
iajs-898	70	2	,	,	PUNCT
iajs-898	70	3	the	the	DET
iajs-898	70	4	marginal	marginal	ADJ
iajs-898	70	5	distribution	distribution	NOUN
iajs-898	70	6	of	of	ADP
iajs-898	70	7	z	z	NOUN
iajs-898	70	8	is	be	AUX
iajs-898	70	9	:	:	PUNCT
iajs-898	70	10	z	z	X
iajs-898	70	11	~	~	PUNCT
iajs-898	70	12	(	(	PUNCT
iajs-898	70	13	0	0	NUM
iajs-898	70	14	,	,	PUNCT
iajs-898	70	15	d+	d+	X
iajs-898	70	16	λi	λi	X
iajs-898	70	17	)	)	PUNCT
iajs-898	70	18	ihjpas	ihjpa	VERB
iajs-898	70	19	ibn	ibn	PROPN
iajs-898	70	20	alhaitham	alhaitham	PROPN
iajs-898	71	1	j.	j.	PROPN
iajs-898	72	1	fo	fo	ADP
iajs-898	72	2	r	r	NOUN
iajs-898	72	3	pure	pure	ADJ
iajs-898	72	4	&	&	CCONJ
iajs-898	72	5	appl	appl	PROPN
iajs-898	72	6	.	.	PUNCT
iajs-898	73	1	sc	sc	PROPN
iajs-898	73	2	i.	i.	PROPN
iajs-898	73	3	vo	vo	PROPN
iajs-898	73	4	l.23	l.23	PROPN
iajs-898	73	5	(	(	PUNCT
iajs-898	73	6	3	3	NUM
iajs-898	73	7	)	)	PUNCT
iajs-898	73	8	2010	2010	NUM
iajs-898	73	9	e(z	e(z	NOUN
iajs-898	73	10	'	'	PUNCT
iajs-898	73	11	z	z	NOUN
iajs-898	73	12	)	)	PUNCT
iajs-898	73	13	=	=	PUNCT
iajs-898	73	14	tr	tr	NOUN
iajs-898	73	15	var(z	var(z	PROPN
iajs-898	73	16	)	)	PUNCT
iajs-898	73	17	=	=	PUNCT
iajs-898	73	18	tr	tr	VERB
iajs-898	73	19	(	(	PUNCT
iajs-898	73	20	d+	d+	X
iajs-898	73	21	λ	λ	X
iajs-898	73	22	i	i	NOUN
iajs-898	73	23	)	)	PUNCT
iajs-898	73	24	=	=	PUNCT
iajs-898	74	1	tr	tr	VERB
iajs-898	74	2	+	+	ADJ
iajs-898	74	3	λ	λ	NOUN
iajs-898	74	4	tr	tr	VERB
iajs-898	74	5	…	…	PUNCT
iajs-898	74	6	..	..	PUNCT
iajs-898	74	7	(	(	PUNCT
iajs-898	74	8	12	12	NUM
iajs-898	74	9	)	)	PUNCT
iajs-898	74	10	where	where	SCONJ
iajs-898	74	11	"	"	PUNCT
iajs-898	74	12	tr	tr	VERB
iajs-898	74	13	"	"	PUNCT
iajs-898	74	14	denotees	denote	VERB
iajs-898	74	15	the	the	DET
iajs-898	74	16	trace	trace	NOUN
iajs-898	74	17	of	of	ADP
iajs-898	74	18	the	the	DET
iajs-898	74	19	matrix.to	matrix.to	X
iajs-898	74	20	find	find	VERB
iajs-898	74	21	an	an	DET
iajs-898	74	22	unbiased	unbiased	ADJ
iajs-898	74	23	estimator	estimator	NOUN
iajs-898	74	24	for	for	ADP
iajs-898	74	25	λ	λ	PROPN
iajs-898	74	26	,	,	PUNCT
iajs-898	74	27	the	the	DET
iajs-898	74	28	following	following	ADJ
iajs-898	74	29	result	result	NOUN
iajs-898	74	30	is	be	AUX
iajs-898	74	31	necessary	necessary	ADJ
iajs-898	74	32	.	.	PUNCT
iajs-898	75	1	(	(	PUNCT
iajs-898	75	2	see[6	see[6	X
iajs-898	75	3	]	]	PUNCT
iajs-898	75	4	)	)	PUNCT
iajs-898	76	1	e	e	X
iajs-898	76	2	(	(	PUNCT
iajs-898	76	3	=	=	SYM
iajs-898	76	4	…	…	PUNCT
iajs-898	76	5	…	…	PUNCT
iajs-898	76	6	..	..	PUNCT
iajs-898	76	7	(	(	PUNCT
iajs-898	76	8	13	13	NUM
iajs-898	76	9	)	)	PUNCT
iajs-898	76	10	where	where	SCONJ
iajs-898	76	11	is	be	AUX
iajs-898	76	12	an	an	DET
iajs-898	76	13	estimator	estimator	NOUN
iajs-898	76	14	of	of	ADP
iajs-898	76	15	.	.	PUNCT
iajs-898	77	1	the	the	DET
iajs-898	77	2	result	result	NOUN
iajs-898	77	3	in	in	ADP
iajs-898	77	4	(	(	PUNCT
iajs-898	77	5	13	13	NUM
iajs-898	77	6	)	)	PUNCT
iajs-898	77	7	can	can	AUX
iajs-898	77	8	be	be	AUX
iajs-898	77	9	easily	easily	ADV
iajs-898	77	10	proved	prove	VERB
iajs-898	77	11	by	by	ADP
iajs-898	77	12	setting	set	VERB
iajs-898	77	13	(	(	PUNCT
iajs-898	77	14	n	n	CCONJ
iajs-898	77	15	-	-	PUNCT
iajs-898	77	16	p1	p1	NOUN
iajs-898	77	17	)	)	PUNCT
iajs-898	77	18	/	/	PUNCT
iajs-898	78	1	=	=	NOUN
iajs-898	78	2	r	r	NOUN
iajs-898	78	3	then	then	ADV
iajs-898	78	4	r	r	NOUN
iajs-898	78	5	~	~	PUNCT
iajs-898	78	6	e	e	X
iajs-898	78	7	(	(	PUNCT
iajs-898	78	8	)	)	PUNCT
iajs-898	78	9	=	=	SYM
iajs-898	78	10	e	e	X
iajs-898	78	11	(	(	PUNCT
iajs-898	78	12	)	)	PUNCT
iajs-898	78	13	=	=	PUNCT
iajs-898	78	14	(	(	PUNCT
iajs-898	78	15	see	see	VERB
iajs-898	78	16	[	[	X
iajs-898	78	17	7	7	NUM
iajs-898	78	18	]	]	PUNCT
iajs-898	78	19	page	page	NOUN
iajs-898	78	20	176	176	NUM
iajs-898	78	21	)	)	PUNCT
iajs-898	78	22	from	from	ADP
iajs-898	78	23	(	(	PUNCT
iajs-898	78	24	12	12	NUM
iajs-898	78	25	)	)	PUNCT
iajs-898	78	26	and	and	CCONJ
iajs-898	78	27	(	(	PUNCT
iajs-898	78	28	13	13	NUM
iajs-898	78	29	)	)	PUNCT
iajs-898	78	30	an	an	DET
iajs-898	78	31	unbiased	unbiased	ADJ
iajs-898	78	32	estimator	estimator	NOUN
iajs-898	78	33	for	for	ADP
iajs-898	78	34	λ	λ	PROPN
iajs-898	78	35	can	can	AUX
iajs-898	78	36	be	be	AUX
iajs-898	78	37	obtained	obtain	VERB
iajs-898	78	38	as	as	ADP
iajs-898	78	39	:	:	PUNCT
iajs-898	78	40	=	=	SYM
iajs-898	78	41	[	[	PUNCT
iajs-898	78	42	(	(	PUNCT
iajs-898	78	43	)	)	PUNCT
iajs-898	78	44	p]/tr	p]/tr	NUM
iajs-898	78	45	or	or	CCONJ
iajs-898	78	46	equivalently	equivalently	ADV
iajs-898	78	47	:	:	PUNCT
iajs-898	78	48	=	=	SYM
iajs-898	79	1	=	=	PUNCT
iajs-898	79	2	[	[	PUNCT
iajs-898	79	3	(	(	PUNCT
iajs-898	79	4	)	)	PUNCT
iajs-898	79	5	–	–	PUNCT
iajs-898	79	6	p]/tr(x'x	p]/tr(x'x	NOUN
iajs-898	79	7	)	)	PUNCT
iajs-898	79	8	…	…	PUNCT
iajs-898	79	9	…	…	PUNCT
iajs-898	79	10	…	…	PUNCT
iajs-898	79	11	.(14	.(14	X
iajs-898	79	12	)	)	PUNCT
iajs-898	79	13	estimating	estimate	VERB
iajs-898	79	14	the	the	DET
iajs-898	79	15	ridge	ridge	NOUN
iajs-898	79	16	parameter_new	parameter_new	ADJ
iajs-898	79	17	approach	approach	NOUN
iajs-898	79	18	:	:	PUNCT
iajs-898	79	19	substituting	substitute	VERB
iajs-898	79	20	from	from	ADP
iajs-898	79	21	δ	δ	PROPN
iajs-898	79	22	in	in	ADP
iajs-898	79	23	formula	formula	NOUN
iajs-898	79	24	(	(	PUNCT
iajs-898	79	25	6	6	NUM
iajs-898	79	26	)	)	PUNCT
iajs-898	79	27	by	by	ADP
iajs-898	79	28	d	d	ADP
iajs-898	79	29	it	it	PRON
iajs-898	79	30	can	can	AUX
iajs-898	79	31	easily	easily	ADV
iajs-898	79	32	be	be	AUX
iajs-898	79	33	shown	show	VERB
iajs-898	79	34	that	that	SCONJ
iajs-898	79	35	the	the	DET
iajs-898	79	36	ordinary	ordinary	ADJ
iajs-898	79	37	ridge	ridge	PROPN
iajs-898	79	38	regression	regression	PROPN
iajs-898	79	39	estimator	estimator	NOUN
iajs-898	79	40	is	be	AUX
iajs-898	79	41	a	a	DET
iajs-898	79	42	member	member	NOUN
iajs-898	79	43	of	of	ADP
iajs-898	79	44	generalized	generalized	ADJ
iajs-898	79	45	shrinkage	shrinkage	NOUN
iajs-898	79	46	estimators	estimator	NOUN
iajs-898	79	47	class	class	NOUN
iajs-898	79	48	,	,	PUNCT
iajs-898	79	49	in	in	ADP
iajs-898	79	50	such	such	DET
iajs-898	79	51	a	a	DET
iajs-898	79	52	case	case	NOUN
iajs-898	79	53	the	the	DET
iajs-898	79	54	shrinkage	shrinkage	NOUN
iajs-898	79	55	factors	factor	NOUN
iajs-898	79	56	of	of	ADP
iajs-898	79	57	ordinary	ordinary	ADJ
iajs-898	79	58	ridge	ridge	NOUN
iajs-898	79	59	regression	regression	PROPN
iajs-898	79	60	estimator	estimator	NOUN
iajs-898	79	61	will	will	AUX
iajs-898	79	62	have	have	VERB
iajs-898	79	63	the	the	DET
iajs-898	79	64	form	form	NOUN
iajs-898	79	65	:	:	PUNCT
iajs-898	79	66	=	=	SYM
iajs-898	79	67	,	,	PUNCT
iajs-898	79	68	1	1	NUM
iajs-898	79	69	…	…	PUNCT
iajs-898	79	70	…	…	PUNCT
iajs-898	79	71	…	…	PUNCT
iajs-898	79	72	(	(	PUNCT
iajs-898	79	73	15	15	NUM
iajs-898	79	74	)	)	PUNCT
iajs-898	79	75	in	in	ADP
iajs-898	79	76	(	(	PUNCT
iajs-898	79	77	1970	1970	NUM
iajs-898	79	78	)	)	PUNCT
iajs-898	79	79	hoerl	hoerl	NOUN
iajs-898	79	80	and	and	CCONJ
iajs-898	79	81	kennard	kennard	NOUN
iajs-898	79	82	proposed	propose	VERB
iajs-898	79	83	an	an	DET
iajs-898	79	84	estimator	estimator	NOUN
iajs-898	79	85	for	for	ADP
iajs-898	79	86	the	the	DET
iajs-898	79	87	ridge	ridge	NOUN
iajs-898	79	88	parameter	parameter	NOUN
iajs-898	79	89	given	give	VERB
iajs-898	79	90	as	as	ADP
iajs-898	79	91	:	:	PUNCT
iajs-898	79	92	=	=	SYM
iajs-898	79	93	…	…	PUNCT
iajs-898	79	94	…	…	PUNCT
iajs-898	79	95	..	..	PUNCT
iajs-898	79	96	(	(	PUNCT
iajs-898	79	97	16	16	NUM
iajs-898	79	98	)	)	PUNCT
iajs-898	79	99	[	[	PUNCT
iajs-898	79	100	see	see	VERB
iajs-898	79	101	[	[	PUNCT
iajs-898	79	102	8	8	NUM
iajs-898	79	103	]	]	PUNCT
iajs-898	79	104	)	)	PUNCT
iajs-898	79	105	in	in	ADP
iajs-898	79	106	this	this	DET
iajs-898	79	107	section	section	NOUN
iajs-898	79	108	,	,	PUNCT
iajs-898	79	109	a	a	DET
iajs-898	79	110	new	new	ADJ
iajs-898	79	111	approach	approach	NOUN
iajs-898	79	112	is	be	AUX
iajs-898	79	113	used	use	VERB
iajs-898	79	114	to	to	PART
iajs-898	79	115	derive	derive	VERB
iajs-898	79	116	given	give	VERB
iajs-898	79	117	in	in	ADP
iajs-898	79	118	(	(	PUNCT
iajs-898	79	119	16	16	NUM
iajs-898	79	120	)	)	PUNCT
iajs-898	79	121	,	,	PUNCT
iajs-898	79	122	this	this	DET
iajs-898	79	123	approach	approach	NOUN
iajs-898	79	124	is	be	AUX
iajs-898	79	125	represented	represent	VERB
iajs-898	79	126	by	by	ADP
iajs-898	79	127	minimizing	minimize	VERB
iajs-898	79	128	mse	mse	NOUN
iajs-898	79	129	(	(	PUNCT
iajs-898	79	130	)	)	PUNCT
iajs-898	79	131	as	as	SCONJ
iajs-898	79	132	follows	follow	VERB
iajs-898	79	133	:	:	PUNCT
iajs-898	79	134	mse	mse	X
iajs-898	79	135	(	(	PUNCT
iajs-898	79	136	)	)	PUNCT
iajs-898	79	137	=	=	SYM
iajs-898	79	138	mse(gδc	mse(gδc	NOUN
iajs-898	79	139	)	)	PUNCT
iajs-898	80	1	=	=	PUNCT
iajs-898	80	2	gmse(δc)g	gmse(δc)g	NOUN
iajs-898	80	3	'	'	PUNCT
iajs-898	80	4	where	where	SCONJ
iajs-898	80	5	mse(δc	mse(δc	NOUN
iajs-898	80	6	)	)	PUNCT
iajs-898	80	7	=	=	PUNCT
iajs-898	81	1	+	+	ADJ
iajs-898	81	2	(	(	PUNCT
iajs-898	81	3	i	i	PROPN
iajs-898	81	4	-	-	PUNCT
iajs-898	81	5	δ)γγ'(i	δ)γγ'(i	NOUN
iajs-898	81	6	-	-	PUNCT
iajs-898	81	7	δ	δ	NOUN
iajs-898	81	8	)	)	PUNCT
iajs-898	81	9	is	be	AUX
iajs-898	81	10	the	the	DET
iajs-898	81	11	mean	mean	ADJ
iajs-898	81	12	squared	square	VERB
iajs-898	81	13	error	error	NOUN
iajs-898	81	14	matrix	matrix	NOUN
iajs-898	81	15	of	of	ADP
iajs-898	81	16	δc	δc	NOUN
iajs-898	81	17	with	with	ADP
iajs-898	81	18	ith	ith	PROPN
iajs-898	81	19	diagonal	diagonal	ADJ
iajs-898	81	20	element	element	NOUN
iajs-898	81	21	given	give	VERB
iajs-898	81	22	as	as	ADP
iajs-898	81	23	:	:	PUNCT
iajs-898	81	24	{	{	PUNCT
iajs-898	81	25	see[3	see[3	ADJ
iajs-898	81	26	]	]	PUNCT
iajs-898	81	27	}	}	PUNCT
iajs-898	81	28	ihjpas	ihjpa	VERB
iajs-898	81	29	ibn	ibn	PROPN
iajs-898	81	30	alhaitham	alhaitham	PROPN
iajs-898	82	1	j.	j.	PROPN
iajs-898	83	1	fo	fo	ADP
iajs-898	83	2	r	r	NOUN
iajs-898	83	3	pure	pure	ADJ
iajs-898	83	4	&	&	CCONJ
iajs-898	83	5	appl	appl	PROPN
iajs-898	83	6	.	.	PUNCT
iajs-898	84	1	sc	sc	PROPN
iajs-898	84	2	i.	i.	PROPN
iajs-898	84	3	vo	vo	PROPN
iajs-898	84	4	l.23	l.23	PROPN
iajs-898	84	5	(	(	PUNCT
iajs-898	84	6	3	3	NUM
iajs-898	84	7	)	)	PUNCT
iajs-898	84	8	2010	2010	NUM
iajs-898	84	9	mse	mse	NOUN
iajs-898	84	10	(	(	PUNCT
iajs-898	84	11	)	)	PUNCT
iajs-898	84	12	=	=	PUNCT
iajs-898	84	13	/	/	PUNCT
iajs-898	85	1	+	+	NUM
iajs-898	85	2	…	…	PUNCT
iajs-898	85	3	…	…	PUNCT
iajs-898	85	4	…	…	PUNCT
iajs-898	85	5	.(17	.(17	PRON
iajs-898	85	6	)	)	PUNCT
iajs-898	85	7	differintiating	differintiate	VERB
iajs-898	85	8	mse	mse	NOUN
iajs-898	85	9	(	(	PUNCT
iajs-898	85	10	)	)	PUNCT
iajs-898	85	11	with	with	ADP
iajs-898	85	12	respect	respect	NOUN
iajs-898	85	13	to	to	ADP
iajs-898	85	14	we	we	PRON
iajs-898	85	15	obtain	obtain	VERB
iajs-898	85	16	:	:	PUNCT
iajs-898	85	17	=	=	SYM
iajs-898	85	18	2	2	NUM
iajs-898	85	19	/	/	SYM
iajs-898	85	20	-2(1	-2(1	NOUN
iajs-898	85	21	)	)	PUNCT
iajs-898	85	22	…	…	PUNCT
iajs-898	85	23	…	…	PUNCT
iajs-898	85	24	…	…	PUNCT
iajs-898	85	25	.	.	PUNCT
iajs-898	86	1	(	(	PUNCT
iajs-898	86	2	18	18	NUM
iajs-898	86	3	)	)	PUNCT
iajs-898	86	4	while	while	SCONJ
iajs-898	86	5	the	the	DET
iajs-898	86	6	second	second	ADJ
iajs-898	86	7	partial	partial	ADJ
iajs-898	86	8	derivative	derivative	NOUN
iajs-898	86	9	is	be	AUX
iajs-898	86	10	nonnegative	nonnegative	ADJ
iajs-898	86	11	constant	constant	ADJ
iajs-898	86	12	it	it	PRON
iajs-898	86	13	follows	follow	VERB
iajs-898	86	14	that	that	SCONJ
iajs-898	86	15	equating	equate	VERB
iajs-898	86	16	the	the	DET
iajs-898	86	17	derivative	derivative	NOUN
iajs-898	86	18	in	in	ADP
iajs-898	86	19	(	(	PUNCT
iajs-898	86	20	18	18	NUM
iajs-898	86	21	)	)	PUNCT
iajs-898	86	22	to	to	ADP
iajs-898	86	23	zero	zero	NUM
iajs-898	86	24	will	will	AUX
iajs-898	86	25	yield	yield	VERB
iajs-898	86	26	a	a	DET
iajs-898	86	27	minimum	minimum	ADJ
iajs-898	86	28	value	value	NOUN
iajs-898	86	29	for	for	ADP
iajs-898	86	30	mse	mse	NOUN
iajs-898	86	31	(	(	PUNCT
iajs-898	86	32	)	)	PUNCT
iajs-898	86	33	this	this	DET
iajs-898	86	34	optimal	optimal	ADJ
iajs-898	86	35	amount	amount	NOUN
iajs-898	86	36	of	of	ADP
iajs-898	86	37	shrinkage	shrinkage	NOUN
iajs-898	86	38	for	for	ADP
iajs-898	86	39	the	the	DET
iajs-898	86	40	i_th	i_th	ADJ
iajs-898	86	41	uncorrelated	uncorrelated	ADJ
iajs-898	86	42	component	component	NOUN
iajs-898	86	43	is	be	AUX
iajs-898	86	44	:	:	PUNCT
iajs-898	86	45	=	=	SYM
iajs-898	86	46	=	=	SYM
iajs-898	86	47	…	…	PUNCT
iajs-898	86	48	…	…	PUNCT
iajs-898	86	49	..	..	PUNCT
iajs-898	86	50	(	(	PUNCT
iajs-898	86	51	19	19	NUM
iajs-898	86	52	)	)	PUNCT
iajs-898	86	53	we	we	PRON
iajs-898	86	54	derive	derive	VERB
iajs-898	86	55	the	the	DET
iajs-898	86	56	formula	formula	NOUN
iajs-898	86	57	in	in	ADP
iajs-898	86	58	(	(	PUNCT
iajs-898	86	59	19	19	NUM
iajs-898	86	60	)	)	PUNCT
iajs-898	86	61	as	as	SCONJ
iajs-898	86	62	follows	follow	VERB
iajs-898	86	63	:	:	PUNCT
iajs-898	86	64	2	2	NUM
iajs-898	86	65	/	/	SYM
iajs-898	86	66	-2(1	-2(1	NOUN
iajs-898	86	67	)	)	PUNCT
iajs-898	87	1	=	=	SYM
iajs-898	87	2	0	0	PUNCT
iajs-898	87	3	then	then	ADV
iajs-898	87	4	/	/	SYM
iajs-898	87	5	=	=	PUNCT
iajs-898	87	6	hence	hence	ADV
iajs-898	87	7	=	=	PUNCT
iajs-898	87	8	which	which	PRON
iajs-898	87	9	implies	imply	VERB
iajs-898	87	10	that	that	SCONJ
iajs-898	87	11	+	+	PUNCT
iajs-898	87	12	=	=	SYM
iajs-898	87	13	then	then	ADV
iajs-898	87	14	solving	solve	VERB
iajs-898	87	15	for	for	ADP
iajs-898	87	16	we	we	PRON
iajs-898	87	17	obtain	obtain	VERB
iajs-898	87	18	the	the	DET
iajs-898	87	19	required	require	VERB
iajs-898	87	20	result	result	NOUN
iajs-898	87	21	.	.	PUNCT
iajs-898	88	1	now	now	ADV
iajs-898	88	2	we	we	PRON
iajs-898	88	3	suggest	suggest	VERB
iajs-898	88	4	comparing	compare	VERB
iajs-898	88	5	the	the	DET
iajs-898	88	6	shrinkage	shrinkage	NOUN
iajs-898	88	7	factor	factor	NOUN
iajs-898	88	8	of	of	ADP
iajs-898	88	9	ridge	ridge	PROPN
iajs-898	88	10	regression	regression	PROPN
iajs-898	88	11	estimator	estimator	NOUN
iajs-898	88	12	given	give	VERB
iajs-898	88	13	in	in	ADP
iajs-898	88	14	equation	equation	NOUN
iajs-898	88	15	(	(	PUNCT
iajs-898	88	16	15	15	NUM
iajs-898	88	17	)	)	PUNCT
iajs-898	88	18	with	with	ADP
iajs-898	88	19	given	give	VERB
iajs-898	88	20	in	in	ADP
iajs-898	88	21	equation	equation	NOUN
iajs-898	88	22	(	(	PUNCT
iajs-898	88	23	19	19	NUM
iajs-898	88	24	)	)	PUNCT
iajs-898	88	25	,	,	PUNCT
iajs-898	88	26	hence	hence	ADV
iajs-898	88	27	we	we	PRON
iajs-898	88	28	conclude	conclude	VERB
iajs-898	88	29	that	that	SCONJ
iajs-898	88	30	the	the	DET
iajs-898	88	31	value	value	NOUN
iajs-898	88	32	of	of	ADP
iajs-898	88	33	the	the	DET
iajs-898	88	34	ridge	ridge	NOUN
iajs-898	88	35	parameter	parameter	PROPN
iajs-898	88	36	k	k	PROPN
iajs-898	88	37	must	must	AUX
iajs-898	88	38	be	be	AUX
iajs-898	88	39	equal	equal	ADJ
iajs-898	88	40	to	to	ADP
iajs-898	88	41	/	/	PUNCT
iajs-898	88	42	.	.	PUNCT
iajs-898	89	1	since	since	SCONJ
iajs-898	89	2	each	each	PRON
iajs-898	89	3	of	of	ADP
iajs-898	89	4	and	and	CCONJ
iajs-898	89	5	is	be	AUX
iajs-898	89	6	unknown	unknown	ADJ
iajs-898	89	7	,	,	PUNCT
iajs-898	89	8	we	we	PRON
iajs-898	89	9	can	can	AUX
iajs-898	89	10	use	use	VERB
iajs-898	89	11	their	their	PRON
iajs-898	89	12	estimated	estimate	VERB
iajs-898	89	13	values	value	NOUN
iajs-898	89	14	,	,	PUNCT
iajs-898	89	15	thus	thus	ADV
iajs-898	89	16	:	:	PUNCT
iajs-898	89	17	=	=	SYM
iajs-898	89	18	=	=	SYM
iajs-898	89	19	…	…	PUNCT
iajs-898	89	20	…	…	PUNCT
iajs-898	89	21	…	…	PUNCT
iajs-898	89	22	.(20	.(20	NOUN
iajs-898	89	23	)	)	PUNCT
iajs-898	89	24	where	where	SCONJ
iajs-898	89	25	is	be	AUX
iajs-898	89	26	the	the	DET
iajs-898	89	27	residual	residual	ADJ
iajs-898	89	28	mean	mean	ADJ
iajs-898	89	29	square	square	NOUN
iajs-898	89	30	in	in	ADP
iajs-898	89	31	the	the	DET
iajs-898	89	32	analysis	analysis	NOUN
iajs-898	89	33	of	of	ADP
iajs-898	89	34	variance	variance	NOUN
iajs-898	89	35	table	table	NOUN
iajs-898	89	36	obtained	obtain	VERB
iajs-898	89	37	from	from	ADP
iajs-898	89	38	the	the	DET
iajs-898	89	39	standard	standard	ADJ
iajs-898	89	40	least	least	ADJ
iajs-898	89	41	squares	square	NOUN
iajs-898	89	42	fit	fit	PROPN
iajs-898	89	43	.	.	PUNCT
iajs-898	90	1	numerical	numerical	ADJ
iajs-898	90	2	example	example	NOUN
iajs-898	90	3	:	:	PUNCT
iajs-898	90	4	in	in	ADP
iajs-898	90	5	this	this	DET
iajs-898	90	6	section	section	NOUN
iajs-898	90	7	a	a	DET
iajs-898	90	8	data	datum	NOUN
iajs-898	90	9	set	set	VERB
iajs-898	90	10	suffer	suffer	VERB
iajs-898	90	11	from	from	ADP
iajs-898	90	12	a	a	DET
iajs-898	90	13	high	high	ADJ
iajs-898	90	14	degree	degree	NOUN
iajs-898	90	15	of	of	ADP
iajs-898	90	16	multicollinearity	multicollinearity	NOUN
iajs-898	90	17	is	be	AUX
iajs-898	90	18	used	use	VERB
iajs-898	90	19	.	.	PUNCT
iajs-898	91	1	the	the	DET
iajs-898	91	2	data	datum	NOUN
iajs-898	91	3	are	be	AUX
iajs-898	91	4	from	from	ADP
iajs-898	91	5	aljibori	aljibori	X
iajs-898	91	6	(	(	PUNCT
iajs-898	91	7	2004	2004	NUM
iajs-898	91	8	)	)	PUNCT
iajs-898	91	9	(	(	PUNCT
iajs-898	91	10	see	see	VERB
iajs-898	91	11	[	[	X
iajs-898	91	12	11	11	NUM
iajs-898	91	13	]	]	PUNCT
iajs-898	91	14	for	for	ADP
iajs-898	91	15	details	detail	NOUN
iajs-898	91	16	)	)	PUNCT
iajs-898	91	17	.	.	PUNCT
iajs-898	92	1	it	it	PRON
iajs-898	92	2	was	be	AUX
iajs-898	92	3	designed	design	VERB
iajs-898	92	4	to	to	PART
iajs-898	92	5	measure	measure	VERB
iajs-898	92	6	the	the	DET
iajs-898	92	7	effect	effect	NOUN
iajs-898	92	8	of	of	ADP
iajs-898	92	9	five	five	NUM
iajs-898	92	10	explanatory	explanatory	ADJ
iajs-898	92	11	variables	variable	NOUN
iajs-898	92	12	,	,	PUNCT
iajs-898	92	13	,	,	PUNCT
iajs-898	92	14	…	…	PUNCT
iajs-898	92	15	.	.	PUNCT
iajs-898	92	16	,	,	PUNCT
iajs-898	92	17	on	on	ADP
iajs-898	92	18	the	the	DET
iajs-898	92	19	response	response	NOUN
iajs-898	92	20	variable	variable	ADJ
iajs-898	92	21	y.	y.	NOUN
iajs-898	92	22	where	where	SCONJ
iajs-898	92	23	the	the	DET
iajs-898	92	24	explanatory	explanatory	ADJ
iajs-898	92	25	variables	variable	NOUN
iajs-898	92	26	represent	represent	VERB
iajs-898	92	27	the	the	DET
iajs-898	92	28	number	number	NOUN
iajs-898	92	29	of	of	ADP
iajs-898	92	30	,	,	PUNCT
iajs-898	92	31	managerials	managerial	NOUN
iajs-898	92	32	,	,	PUNCT
iajs-898	92	33	technicians	technician	NOUN
iajs-898	92	34	,	,	PUNCT
iajs-898	92	35	skilled	skilled	ADJ
iajs-898	92	36	workers	worker	NOUN
iajs-898	92	37	,	,	PUNCT
iajs-898	92	38	unskilled	unskilled	ADJ
iajs-898	92	39	workers	worker	NOUN
iajs-898	92	40	and	and	CCONJ
iajs-898	92	41	service	service	NOUN
iajs-898	92	42	workers	worker	NOUN
iajs-898	92	43	respectively	respectively	ADV
iajs-898	92	44	,	,	PUNCT
iajs-898	92	45	while	while	SCONJ
iajs-898	92	46	the	the	DET
iajs-898	92	47	response	response	NOUN
iajs-898	92	48	variable	variable	ADJ
iajs-898	92	49	y	y	PROPN
iajs-898	92	50	represents	represent	VERB
iajs-898	92	51	the	the	DET
iajs-898	92	52	productivity	productivity	NOUN
iajs-898	92	53	of	of	ADP
iajs-898	92	54	the	the	DET
iajs-898	92	55	industrial	industrial	ADJ
iajs-898	92	56	sector	sector	NOUN
iajs-898	92	57	in	in	ADP
iajs-898	92	58	iraq	iraq	PROPN
iajs-898	92	59	measured	measure	VERB
iajs-898	92	60	by	by	ADP
iajs-898	92	61	the	the	DET
iajs-898	92	62	surplus	surplus	ADJ
iajs-898	92	63	value	value	NOUN
iajs-898	92	64	method	method	NOUN
iajs-898	92	65	for	for	ADP
iajs-898	92	66	the	the	DET
iajs-898	92	67	period	period	NOUN
iajs-898	92	68	21	21	NUM
iajs-898	92	69	years	year	NOUN
iajs-898	92	70	from	from	ADP
iajs-898	92	71	1970	1970	NUM
iajs-898	92	72	to	to	ADP
iajs-898	92	73	1990.our	1990.our	NUM
iajs-898	92	74	purpose	purpose	NOUN
iajs-898	92	75	is	be	AUX
iajs-898	92	76	only	only	ADV
iajs-898	92	77	to	to	PART
iajs-898	92	78	compare	compare	VERB
iajs-898	92	79	the	the	DET
iajs-898	92	80	performance	performance	NOUN
iajs-898	92	81	of	of	ADP
iajs-898	92	82	bayesian	bayesian	NOUN
iajs-898	92	83	and	and	CCONJ
iajs-898	92	84	conventional	conventional	ADJ
iajs-898	92	85	estimators	estimator	NOUN
iajs-898	92	86	for	for	ADP
iajs-898	92	87	the	the	DET
iajs-898	92	88	ridge	ridge	NOUN
iajs-898	92	89	parameter	parameter	NOUN
iajs-898	92	90	.	.	PUNCT
iajs-898	93	1	the	the	DET
iajs-898	93	2	original	original	ADJ
iajs-898	93	3	data	datum	NOUN
iajs-898	93	4	are	be	AUX
iajs-898	93	5	presented	present	VERB
iajs-898	93	6	in	in	ADP
iajs-898	93	7	table	table	NOUN
iajs-898	93	8	(	(	PUNCT
iajs-898	93	9	1	1	NUM
iajs-898	93	10	)	)	PUNCT
iajs-898	93	11	.	.	PUNCT
iajs-898	94	1	ihjpas	ihjpa	VERB
iajs-898	94	2	ibn	ibn	PROPN
iajs-898	94	3	alhaitham	alhaitham	PROPN
iajs-898	95	1	j.	j.	PROPN
iajs-898	96	1	fo	fo	ADP
iajs-898	96	2	r	r	NOUN
iajs-898	96	3	pure	pure	ADJ
iajs-898	96	4	&	&	CCONJ
iajs-898	96	5	appl	appl	PROPN
iajs-898	96	6	.	.	PUNCT
iajs-898	97	1	sc	sc	PROPN
iajs-898	97	2	i.	i.	PROPN
iajs-898	97	3	vo	vo	PROPN
iajs-898	97	4	l.23	l.23	PROPN
iajs-898	97	5	(	(	PUNCT
iajs-898	97	6	3	3	NUM
iajs-898	97	7	)	)	PUNCT
iajs-898	97	8	2010	2010	NUM
iajs-898	97	9	for	for	ADP
iajs-898	97	10	this	this	DET
iajs-898	97	11	data	datum	NOUN
iajs-898	97	12	we	we	PRON
iajs-898	97	13	found	find	VERB
iajs-898	97	14	that	that	SCONJ
iajs-898	97	15	the	the	DET
iajs-898	97	16	estimated	estimate	VERB
iajs-898	97	17	value	value	NOUN
iajs-898	97	18	of	of	ADP
iajs-898	97	19	the	the	DET
iajs-898	97	20	ridge	ridge	NOUN
iajs-898	97	21	parameter	parameter	NOUN
iajs-898	97	22	was	be	AUX
iajs-898	97	23	=	=	NUM
iajs-898	97	24	0.125260	0.125260	NUM
iajs-898	97	25	and	and	CCONJ
iajs-898	97	26	=	=	SYM
iajs-898	97	27	0.494107	0.494107	NUM
iajs-898	97	28	obtained	obtain	VERB
iajs-898	97	29	by	by	ADP
iajs-898	97	30	applying	apply	VERB
iajs-898	97	31	the	the	DET
iajs-898	97	32	formula	formula	NOUN
iajs-898	97	33	in	in	ADP
iajs-898	97	34	(	(	PUNCT
iajs-898	97	35	14	14	NUM
iajs-898	97	36	)	)	PUNCT
iajs-898	97	37	and	and	CCONJ
iajs-898	97	38	in	in	ADP
iajs-898	97	39	(	(	PUNCT
iajs-898	97	40	20	20	NUM
iajs-898	97	41	)	)	PUNCT
iajs-898	97	42	respectively	respectively	ADV
iajs-898	97	43	.	.	PUNCT
iajs-898	98	1	in	in	ADP
iajs-898	98	2	order	order	NOUN
iajs-898	98	3	to	to	PART
iajs-898	98	4	make	make	VERB
iajs-898	98	5	the	the	DET
iajs-898	98	6	ridge	ridge	NOUN
iajs-898	98	7	regression	regression	NOUN
iajs-898	98	8	analysis	analysis	NOUN
iajs-898	98	9	we	we	PRON
iajs-898	98	10	used	use	VERB
iajs-898	98	11	the	the	DET
iajs-898	98	12	numeric	numeric	ADJ
iajs-898	98	13	calculation	calculation	NOUN
iajs-898	98	14	statistical	statistical	ADJ
iajs-898	98	15	system	system	NOUN
iajs-898	98	16	(	(	PUNCT
iajs-898	98	17	ncss	ncss	PROPN
iajs-898	98	18	)	)	PUNCT
iajs-898	98	19	and	and	CCONJ
iajs-898	98	20	the	the	DET
iajs-898	98	21	results	result	NOUN
iajs-898	98	22	were	be	AUX
iajs-898	98	23	given	give	VERB
iajs-898	98	24	in	in	ADP
iajs-898	98	25	table	table	NOUN
iajs-898	98	26	(	(	PUNCT
iajs-898	98	27	2	2	NUM
iajs-898	98	28	)	)	PUNCT
iajs-898	98	29	through	through	ADP
iajs-898	98	30	table	table	NOUN
iajs-898	98	31	(	(	PUNCT
iajs-898	98	32	6	6	NUM
iajs-898	98	33	)	)	PUNCT
iajs-898	98	34	.	.	PUNCT
iajs-898	99	1	pearson	pearson	PROPN
iajs-898	99	2	correlations	correlation	NOUN
iajs-898	99	3	were	be	AUX
iajs-898	99	4	given	give	VERB
iajs-898	99	5	for	for	ADP
iajs-898	99	6	all	all	DET
iajs-898	99	7	variables	variable	NOUN
iajs-898	99	8	in	in	ADP
iajs-898	99	9	table	table	NOUN
iajs-898	99	10	(	(	PUNCT
iajs-898	99	11	2	2	NUM
iajs-898	99	12	)	)	PUNCT
iajs-898	99	13	.	.	PUNCT
iajs-898	100	1	these	these	DET
iajs-898	100	2	correlation	correlation	NOUN
iajs-898	100	3	coefficients	coefficient	NOUN
iajs-898	100	4	show	show	VERB
iajs-898	100	5	which	which	PRON
iajs-898	100	6	explanatory	explanatory	ADJ
iajs-898	100	7	variables	variable	NOUN
iajs-898	100	8	are	be	AUX
iajs-898	100	9	highly	highly	ADV
iajs-898	100	10	correlated	correlate	VERB
iajs-898	100	11	with	with	ADP
iajs-898	100	12	response	response	NOUN
iajs-898	100	13	variable	variable	NOUN
iajs-898	100	14	and	and	CCONJ
iajs-898	100	15	with	with	ADP
iajs-898	100	16	each	each	DET
iajs-898	100	17	other	other	ADJ
iajs-898	100	18	.	.	PUNCT
iajs-898	101	1	explanatory	explanatory	ADJ
iajs-898	101	2	variables	variable	NOUN
iajs-898	101	3	that	that	PRON
iajs-898	101	4	are	be	AUX
iajs-898	101	5	highly	highly	ADV
iajs-898	101	6	correlated	correlate	VERB
iajs-898	101	7	with	with	ADP
iajs-898	101	8	one	one	NOUN
iajs-898	101	9	another	another	PRON
iajs-898	101	10	may	may	AUX
iajs-898	101	11	cause	cause	VERB
iajs-898	101	12	multicollinearity	multicollinearity	NOUN
iajs-898	101	13	problem	problem	NOUN
iajs-898	101	14	.	.	PUNCT
iajs-898	102	1	table	table	NOUN
iajs-898	102	2	(	(	PUNCT
iajs-898	102	3	3	3	X
iajs-898	102	4	)	)	PUNCT
iajs-898	102	5	gives	give	VERB
iajs-898	102	6	an	an	DET
iajs-898	102	7	eigenvalue	eigenvalue	ADJ
iajs-898	102	8	analysis	analysis	NOUN
iajs-898	102	9	of	of	ADP
iajs-898	102	10	the	the	DET
iajs-898	102	11	explanatory	explanatory	ADJ
iajs-898	102	12	variables	variable	NOUN
iajs-898	102	13	after	after	SCONJ
iajs-898	102	14	they	they	PRON
iajs-898	102	15	have	have	AUX
iajs-898	102	16	been	be	AUX
iajs-898	102	17	centerd	centerd	ADJ
iajs-898	102	18	and	and	CCONJ
iajs-898	102	19	scaled	scale	VERB
iajs-898	102	20	.	.	PUNCT
iajs-898	103	1	notice	notice	VERB
iajs-898	103	2	that	that	SCONJ
iajs-898	103	3	incremental	incremental	ADJ
iajs-898	103	4	percent	percent	NOUN
iajs-898	103	5	is	be	AUX
iajs-898	103	6	the	the	DET
iajs-898	103	7	percent	percent	NOUN
iajs-898	103	8	this	this	DET
iajs-898	103	9	eigenvalue	eigenvalue	NOUN
iajs-898	103	10	is	be	AUX
iajs-898	103	11	of	of	ADP
iajs-898	103	12	the	the	DET
iajs-898	103	13	total	total	NOUN
iajs-898	103	14	.	.	PUNCT
iajs-898	104	1	percents	percent	NOUN
iajs-898	104	2	near	near	ADP
iajs-898	104	3	zero	zero	NUM
iajs-898	104	4	indicate	indicate	VERB
iajs-898	104	5	a	a	DET
iajs-898	104	6	multicollinearity	multicollinearity	NOUN
iajs-898	104	7	problem	problem	NOUN
iajs-898	104	8	.	.	PUNCT
iajs-898	105	1	the	the	DET
iajs-898	105	2	condition	condition	NOUN
iajs-898	105	3	number	number	NOUN
iajs-898	105	4	is	be	AUX
iajs-898	105	5	the	the	DET
iajs-898	105	6	largest	large	ADJ
iajs-898	105	7	eigenvalue	eigenvalue	NOUN
iajs-898	105	8	divided	divide	VERB
iajs-898	105	9	by	by	ADP
iajs-898	105	10	each	each	DET
iajs-898	105	11	corresponding	correspond	VERB
iajs-898	105	12	eigenvalue	eigenvalue	PROPN
iajs-898	105	13	.	.	PUNCT
iajs-898	106	1	condition	condition	NOUN
iajs-898	106	2	numbers	number	NOUN
iajs-898	106	3	more	more	ADJ
iajs-898	106	4	than	than	ADP
iajs-898	106	5	100	100	NUM
iajs-898	106	6	indicates	indicate	VERB
iajs-898	106	7	multicollinearity	multicollinearity	NOUN
iajs-898	106	8	problem	problem	NOUN
iajs-898	106	9	.	.	PUNCT
iajs-898	107	1	(	(	PUNCT
iajs-898	107	2	see[9	see[9	NUM
iajs-898	107	3	]	]	PUNCT
iajs-898	107	4	)	)	PUNCT
iajs-898	107	5	.	.	PUNCT
iajs-898	108	1	conclusions	conclusion	NOUN
iajs-898	108	2	:	:	PUNCT
iajs-898	108	3	1_a	1_a	NUM
iajs-898	108	4	new	new	ADJ
iajs-898	108	5	approach	approach	NOUN
iajs-898	108	6	for	for	ADP
iajs-898	108	7	estimating	estimate	VERB
iajs-898	108	8	the	the	DET
iajs-898	108	9	ridge	ridge	NOUN
iajs-898	108	10	parameter	parameter	NOUN
iajs-898	108	11	was	be	AUX
iajs-898	108	12	introduced	introduce	VERB
iajs-898	108	13	by	by	ADP
iajs-898	108	14	using	use	VERB
iajs-898	108	15	the	the	DET
iajs-898	108	16	singular	singular	ADJ
iajs-898	108	17	value	value	NOUN
iajs-898	108	18	decomposition	decomposition	NOUN
iajs-898	108	19	technique	technique	NOUN
iajs-898	108	20	.	.	PUNCT
iajs-898	109	1	2_in	2_in	NOUN
iajs-898	109	2	their	their	PRON
iajs-898	109	3	development	development	NOUN
iajs-898	109	4	of	of	ADP
iajs-898	109	5	ridge	ridge	PROPN
iajs-898	109	6	regression	regression	PROPN
iajs-898	109	7	,	,	PUNCT
iajs-898	109	8	hoerl	hoerl	NOUN
iajs-898	109	9	and	and	CCONJ
iajs-898	109	10	kennard	kennard	NOUN
iajs-898	109	11	focus	focus	VERB
iajs-898	109	12	attention	attention	NOUN
iajs-898	109	13	on	on	ADP
iajs-898	109	14	the	the	DET
iajs-898	109	15	eigenvalues	eigenvalue	NOUN
iajs-898	109	16	of	of	ADP
iajs-898	109	17	the	the	DET
iajs-898	109	18	information	information	NOUN
iajs-898	109	19	matrix	matrix	NOUN
iajs-898	109	20	x'x	x'x	VERB
iajs-898	109	21	.	.	PUNCT
iajs-898	110	1	a	a	DET
iajs-898	110	2	seriously	seriously	ADV
iajs-898	110	3	non	non	ADJ
iajs-898	110	4	orthogonal	orthogonal	ADJ
iajs-898	110	5	problem	problem	NOUN
iajs-898	110	6	is	be	AUX
iajs-898	110	7	characterized	characterize	VERB
iajs-898	110	8	by	by	ADP
iajs-898	110	9	the	the	DET
iajs-898	110	10	fact	fact	NOUN
iajs-898	110	11	that	that	SCONJ
iajs-898	110	12	the	the	DET
iajs-898	110	13	smallest	small	ADJ
iajs-898	110	14	eigenvalue	eigenvalue	NOUN
iajs-898	110	15	is	be	AUX
iajs-898	110	16	very	very	ADV
iajs-898	110	17	much	much	ADV
iajs-898	110	18	smaller	small	ADJ
iajs-898	110	19	than	than	ADP
iajs-898	110	20	unity	unity	NOUN
iajs-898	110	21	.	.	PUNCT
iajs-898	111	1	in	in	ADP
iajs-898	111	2	our	our	PRON
iajs-898	111	3	problem	problem	NOUN
iajs-898	111	4	the	the	DET
iajs-898	111	5	smallest	small	ADJ
iajs-898	111	6	eigenvalue	eigenvalue	NOUN
iajs-898	111	7	is	be	AUX
iajs-898	111	8	0.0093	0.0093	NUM
iajs-898	111	9	this	this	PRON
iajs-898	111	10	indicates	indicate	VERB
iajs-898	111	11	that	that	SCONJ
iajs-898	111	12	our	our	PRON
iajs-898	111	13	data	datum	NOUN
iajs-898	111	14	set	set	VERB
iajs-898	111	15	suffer	suffer	VERB
iajs-898	111	16	from	from	ADP
iajs-898	111	17	a	a	DET
iajs-898	111	18	high	high	ADJ
iajs-898	111	19	degree	degree	NOUN
iajs-898	111	20	of	of	ADP
iajs-898	111	21	multicollinearity	multicollinearity	NOUN
iajs-898	111	22	.	.	PUNCT
iajs-898	112	1	3_it	3_it	NUM
iajs-898	112	2	should	should	AUX
iajs-898	112	3	also	also	ADV
iajs-898	112	4	be	be	AUX
iajs-898	112	5	noted	note	VERB
iajs-898	112	6	that	that	SCONJ
iajs-898	112	7	the	the	DET
iajs-898	112	8	variance	variance	NOUN
iajs-898	112	9	inflation	inflation	NOUN
iajs-898	112	10	factor	factor	NOUN
iajs-898	112	11	(	(	PUNCT
iajs-898	112	12	vif	vif	NOUN
iajs-898	112	13	)	)	PUNCT
iajs-898	112	14	is	be	AUX
iajs-898	112	15	an	an	DET
iajs-898	112	16	additional	additional	ADJ
iajs-898	112	17	measure	measure	NOUN
iajs-898	112	18	of	of	ADP
iajs-898	112	19	multicollinearity	multicollinearity	NOUN
iajs-898	112	20	.	.	PUNCT
iajs-898	113	1	it	it	PRON
iajs-898	113	2	is	be	AUX
iajs-898	113	3	the	the	DET
iajs-898	113	4	reciprocal	reciprocal	NOUN
iajs-898	113	5	of	of	ADP
iajs-898	113	6	(	(	PUNCT
iajs-898	113	7	1	1	NUM
iajs-898	113	8	)	)	PUNCT
iajs-898	113	9	where	where	SCONJ
iajs-898	113	10	is	be	AUX
iajs-898	113	11	the	the	DET
iajs-898	113	12	square	square	ADJ
iajs-898	113	13	value	value	NOUN
iajs-898	113	14	of	of	ADP
iajs-898	113	15	the	the	DET
iajs-898	113	16	multiple	multiple	ADJ
iajs-898	113	17	correlation	correlation	NOUN
iajs-898	113	18	coefficient	coefficient	NOUN
iajs-898	113	19	between	between	ADP
iajs-898	113	20	the	the	DET
iajs-898	113	21	explanatory	explanatory	ADJ
iajs-898	113	22	variable	variable	NOUN
iajs-898	113	23	and	and	CCONJ
iajs-898	113	24	other	other	ADJ
iajs-898	113	25	explanatory	explanatory	ADJ
iajs-898	113	26	variables	variable	NOUN
iajs-898	113	27	.	.	PUNCT
iajs-898	114	1	a	a	DET
iajs-898	114	2	vif	vif	NOUN
iajs-898	114	3	of	of	ADP
iajs-898	114	4	10	10	NUM
iajs-898	114	5	or	or	CCONJ
iajs-898	114	6	more	more	ADJ
iajs-898	114	7	indicates	indicate	VERB
iajs-898	114	8	a	a	DET
iajs-898	114	9	multicollinearity	multicollinearity	NOUN
iajs-898	114	10	problem	problem	NOUN
iajs-898	114	11	.	.	PUNCT
iajs-898	115	1	(	(	PUNCT
iajs-898	115	2	see[10	see[10	NUM
iajs-898	115	3	]	]	PUNCT
iajs-898	115	4	)	)	PUNCT
iajs-898	115	5	.	.	PUNCT
iajs-898	116	1	in	in	ADP
iajs-898	116	2	our	our	PRON
iajs-898	116	3	problem	problem	NOUN
iajs-898	116	4	the	the	DET
iajs-898	116	5	largest	large	ADJ
iajs-898	116	6	vif	vif	NOUN
iajs-898	116	7	value	value	NOUN
iajs-898	116	8	is	be	AUX
iajs-898	116	9	65.16	65.16	NUM
iajs-898	116	10	which	which	PRON
iajs-898	116	11	is	be	AUX
iajs-898	116	12	an	an	DET
iajs-898	116	13	additional	additional	ADJ
iajs-898	116	14	indicator	indicator	NOUN
iajs-898	116	15	that	that	PRON
iajs-898	116	16	our	our	PRON
iajs-898	116	17	data	datum	NOUN
iajs-898	116	18	set	set	VERB
iajs-898	116	19	suffer	suffer	VERB
iajs-898	116	20	from	from	ADP
iajs-898	116	21	a	a	DET
iajs-898	116	22	high	high	ADJ
iajs-898	116	23	degree	degree	NOUN
iajs-898	116	24	of	of	ADP
iajs-898	116	25	multicollinearity	multicollinearity	NOUN
iajs-898	116	26	.	.	PUNCT
iajs-898	117	1	4	4	NUM
iajs-898	117	2	_	_	NOUN
iajs-898	117	3	since	since	SCONJ
iajs-898	117	4	one	one	NUM
iajs-898	117	5	of	of	ADP
iajs-898	117	6	the	the	DET
iajs-898	117	7	objects	object	NOUN
iajs-898	117	8	of	of	ADP
iajs-898	117	9	ridge	ridge	NOUN
iajs-898	117	10	regression	regression	NOUN
iajs-898	117	11	is	be	AUX
iajs-898	117	12	to	to	PART
iajs-898	117	13	reduce	reduce	VERB
iajs-898	117	14	the	the	DET
iajs-898	117	15	standard	standard	ADJ
iajs-898	117	16	error	error	NOUN
iajs-898	117	17	of	of	ADP
iajs-898	117	18	the	the	DET
iajs-898	117	19	regression	regression	NOUN
iajs-898	117	20	coefficients	coefficient	NOUN
iajs-898	117	21	,	,	PUNCT
iajs-898	117	22	it	it	PRON
iajs-898	117	23	is	be	AUX
iajs-898	117	24	of	of	ADP
iajs-898	117	25	interest	interest	NOUN
iajs-898	117	26	to	to	PART
iajs-898	117	27	see	see	VERB
iajs-898	117	28	how	how	SCONJ
iajs-898	117	29	much	much	ADJ
iajs-898	117	30	reduction	reduction	NOUN
iajs-898	117	31	has	have	AUX
iajs-898	117	32	taken	take	VERB
iajs-898	117	33	place	place	NOUN
iajs-898	117	34	.	.	PUNCT
iajs-898	118	1	for	for	ADP
iajs-898	118	2	our	our	PRON
iajs-898	118	3	problem	problem	NOUN
iajs-898	118	4	it	it	PRON
iajs-898	118	5	should	should	AUX
iajs-898	118	6	be	be	AUX
iajs-898	118	7	noted	note	VERB
iajs-898	118	8	from	from	ADP
iajs-898	118	9	table	table	NOUN
iajs-898	118	10	(	(	PUNCT
iajs-898	118	11	5	5	NUM
iajs-898	118	12	)	)	PUNCT
iajs-898	118	13	and	and	CCONJ
iajs-898	118	14	table	table	NOUN
iajs-898	118	15	(	(	PUNCT
iajs-898	118	16	6	6	NUM
iajs-898	118	17	)	)	PUNCT
iajs-898	118	18	that	that	SCONJ
iajs-898	118	19	the	the	DET
iajs-898	118	20	standard	standard	ADJ
iajs-898	118	21	errors	error	NOUN
iajs-898	118	22	for	for	ADP
iajs-898	118	23	the	the	DET
iajs-898	118	24	ridge	ridge	NOUN
iajs-898	118	25	regression	regression	NOUN
iajs-898	118	26	coefficients	coefficient	NOUN
iajs-898	118	27	are	be	AUX
iajs-898	118	28	less	less	ADJ
iajs-898	118	29	than	than	ADP
iajs-898	118	30	the	the	DET
iajs-898	118	31	corresponding	corresponding	ADJ
iajs-898	118	32	standard	standard	ADJ
iajs-898	118	33	errors	error	NOUN
iajs-898	118	34	for	for	ADP
iajs-898	118	35	least	least	ADJ
iajs-898	118	36	squares	square	NOUN
iajs-898	118	37	coefficients	coefficient	NOUN
iajs-898	118	38	.	.	PUNCT
iajs-898	119	1	also	also	ADV
iajs-898	119	2	we	we	PRON
iajs-898	119	3	note	note	VERB
iajs-898	119	4	that	that	SCONJ
iajs-898	119	5	the	the	DET
iajs-898	119	6	standard	standard	ADJ
iajs-898	119	7	errors	error	NOUN
iajs-898	119	8	for	for	ADP
iajs-898	119	9	the	the	DET
iajs-898	119	10	ridge	ridge	NOUN
iajs-898	119	11	regression	regression	PROPN
iajs-898	119	12	coefficients	coefficient	NOUN
iajs-898	119	13	obtained	obtain	VERB
iajs-898	119	14	by	by	ADP
iajs-898	119	15	using	use	VERB
iajs-898	119	16	the	the	DET
iajs-898	119	17	conventional	conventional	ADJ
iajs-898	119	18	mehod	mehod	NOUN
iajs-898	119	19	for	for	ADP
iajs-898	119	20	estimating	estimate	VERB
iajs-898	119	21	the	the	DET
iajs-898	119	22	ridge	ridge	NOUN
iajs-898	119	23	parameter	parameter	NOUN
iajs-898	119	24	are	be	AUX
iajs-898	119	25	less	less	ADJ
iajs-898	119	26	than	than	ADP
iajs-898	119	27	the	the	DET
iajs-898	119	28	corresponding	corresponding	ADJ
iajs-898	119	29	standard	standard	ADJ
iajs-898	119	30	errors	error	NOUN
iajs-898	119	31	for	for	ADP
iajs-898	119	32	the	the	DET
iajs-898	119	33	ridge	ridge	NOUN
iajs-898	119	34	regression	regression	PROPN
iajs-898	119	35	coefficients	coefficient	NOUN
iajs-898	119	36	obtained	obtain	VERB
iajs-898	119	37	by	by	ADP
iajs-898	119	38	using	use	VERB
iajs-898	119	39	the	the	DET
iajs-898	119	40	bayesian	bayesian	NOUN
iajs-898	119	41	method	method	NOUN
iajs-898	119	42	.	.	PUNCT
iajs-898	120	1	from	from	ADP
iajs-898	120	2	this	this	DET
iajs-898	120	3	comparison	comparison	NOUN
iajs-898	120	4	we	we	PRON
iajs-898	120	5	conclude	conclude	VERB
iajs-898	120	6	that	that	SCONJ
iajs-898	120	7	the	the	DET
iajs-898	120	8	conventional	conventional	ADJ
iajs-898	120	9	mehod	mehod	NOUN
iajs-898	120	10	is	be	AUX
iajs-898	120	11	performed	perform	VERB
iajs-898	120	12	better	well	ADV
iajs-898	120	13	than	than	ADP
iajs-898	120	14	the	the	DET
iajs-898	120	15	bayesian	bayesian	NOUN
iajs-898	120	16	method	method	NOUN
iajs-898	120	17	for	for	ADP
iajs-898	120	18	this	this	DET
iajs-898	120	19	data	datum	NOUN
iajs-898	120	20	set	set	VERB
iajs-898	120	21	.	.	PUNCT
iajs-898	121	1	ihjpas	ihjpa	VERB
iajs-898	121	2	ibn	ibn	PROPN
iajs-898	121	3	alhaitham	alhaitham	PROPN
iajs-898	122	1	j.	j.	PROPN
iajs-898	123	1	fo	fo	ADP
iajs-898	123	2	r	r	NOUN
iajs-898	123	3	pure	pure	ADJ
iajs-898	123	4	&	&	CCONJ
iajs-898	123	5	appl	appl	PROPN
iajs-898	123	6	.	.	PUNCT
iajs-898	124	1	sc	sc	PROPN
iajs-898	124	2	i.	i.	PROPN
iajs-898	124	3	vo	vo	PROPN
iajs-898	124	4	l.23	l.23	PROPN
iajs-898	124	5	(	(	PUNCT
iajs-898	124	6	3	3	NUM
iajs-898	124	7	)	)	PUNCT
iajs-898	124	8	2010	2010	NUM
iajs-898	124	9	references	reference	NOUN
iajs-898	124	10	:	:	PUNCT
iajs-898	124	11	1	1	X
iajs-898	124	12	.	.	X
iajs-898	124	13	draper	draper	NOUN
iajs-898	124	14	,	,	PUNCT
iajs-898	124	15	n.r	n.r	PROPN
iajs-898	124	16	.	.	PROPN
iajs-898	124	17	and	and	CCONJ
iajs-898	124	18	smith	smith	PROPN
iajs-898	124	19	,	,	PUNCT
iajs-898	124	20	h.	h.	PROPN
iajs-898	124	21	(	(	PUNCT
iajs-898	124	22	1981),"applied	1981),"applie	VERB
iajs-898	124	23	regression	regression	NOUN
iajs-898	124	24	analysis	analysis	NOUN
iajs-898	124	25	"	"	PUNCT
iajs-898	124	26	second	second	ADJ
iajs-898	124	27	edition	edition	NOUN
iajs-898	124	28	,	,	PUNCT
iajs-898	124	29	john	john	PROPN
iajs-898	124	30	wiley	wiley	PROPN
iajs-898	124	31	and	and	CCONJ
iajs-898	124	32	sons	son	NOUN
iajs-898	124	33	,	,	PUNCT
iajs-898	124	34	new	new	PROPN
iajs-898	124	35	york	york	PROPN
iajs-898	124	36	.	.	PUNCT
iajs-898	125	1	2.goldstein	2.goldstein	NUM
iajs-898	125	2	,	,	PUNCT
iajs-898	125	3	m.	m.	NOUN
iajs-898	125	4	and	and	CCONJ
iajs-898	125	5	smith	smith	PROPN
iajs-898	125	6	,	,	PUNCT
iajs-898	125	7	a.f.m.(1974),"ridge	a.f.m.(1974),"ridge	PROPN
iajs-898	125	8	type	type	NOUN
iajs-898	125	9	estimator	estimator	NOUN
iajs-898	125	10	for	for	ADP
iajs-898	125	11	regression	regression	NOUN
iajs-898	125	12	analysis	analysis	NOUN
iajs-898	125	13	.	.	PUNCT
iajs-898	126	1	"jornal	"jornal	ADJ
iajs-898	126	2	royal	royal	ADJ
iajs-898	126	3	statistical	statistical	ADJ
iajs-898	126	4	society	society	NOUN
iajs-898	126	5	.	.	PUNCT
iajs-898	127	1	b36,284_291	b36,284_291	PROPN
iajs-898	127	2	.	.	PUNCT
iajs-898	128	1	3.rao	3.rao	ADJ
iajs-898	128	2	,	,	PUNCT
iajs-898	128	3	c.r.(1973),"linear	c.r.(1973),"linear	VERB
iajs-898	128	4	statistical	statistical	ADJ
iajs-898	128	5	inference	inference	NOUN
iajs-898	128	6	and	and	CCONJ
iajs-898	128	7	its	its	PRON
iajs-898	128	8	applications	application	NOUN
iajs-898	128	9	.	.	PUNCT
iajs-898	129	1	"second	"second	PROPN
iajs-898	129	2	edition	edition	PROPN
iajs-898	129	3	,	,	PUNCT
iajs-898	129	4	john	john	PROPN
iajs-898	129	5	wiley	wiley	PROPN
iajs-898	129	6	and	and	CCONJ
iajs-898	129	7	sons	son	NOUN
iajs-898	129	8	.	.	PUNCT
iajs-898	130	1	new	new	PROPN
iajs-898	130	2	york	york	PROPN
iajs-898	130	3	.	.	PUNCT
iajs-898	131	1	4.obenchain	4.obenchain	NUM
iajs-898	131	2	,	,	PUNCT
iajs-898	131	3	r.l	r.l	PROPN
iajs-898	131	4	.	.	PROPN
iajs-898	131	5	(	(	PUNCT
iajs-898	131	6	1978),"good	1978),"good	NOUN
iajs-898	131	7	and	and	CCONJ
iajs-898	131	8	optimal	optimal	ADJ
iajs-898	131	9	ridge	ridge	NOUN
iajs-898	131	10	estimators	estimator	NOUN
iajs-898	131	11	.	.	PUNCT
iajs-898	131	12	"	"	PUNCT
iajs-898	132	1	ann	ann	PROPN
iajs-898	132	2	.	.	PROPN
iajs-898	132	3	statist.6,1111_1121	statist.6,1111_1121	PROPN
iajs-898	132	4	.	.	PUNCT
iajs-898	133	1	5.lindley	5.lindley	NUM
iajs-898	133	2	,	,	PUNCT
iajs-898	133	3	d.v	d.v	PROPN
iajs-898	133	4	.	.	PROPN
iajs-898	133	5	and	and	CCONJ
iajs-898	133	6	smith	smith	PROPN
iajs-898	133	7	,	,	PUNCT
iajs-898	133	8	a.f.m.(1972),"bayes	a.f.m.(1972),"bayes	PROPN
iajs-898	133	9	estimators	estimator	NOUN
iajs-898	133	10	for	for	ADP
iajs-898	133	11	the	the	DET
iajs-898	133	12	linear	linear	PROPN
iajs-898	133	13	model	model	NOUN
iajs-898	133	14	.	.	PUNCT
iajs-898	134	1	"jornal	"jornal	ADJ
iajs-898	134	2	royal	royal	ADJ
iajs-898	134	3	statistical	statistical	ADJ
iajs-898	134	4	society	society	NOUN
iajs-898	134	5	.	.	PUNCT
iajs-898	135	1	b34	b34	PROPN
iajs-898	135	2	,	,	PUNCT
iajs-898	135	3	1_41	1_41	NUM
iajs-898	135	4	.	.	PUNCT
iajs-898	136	1	.srivastava	.srivastava	PROPN
iajs-898	136	2	,	,	PUNCT
iajs-898	136	3	m.i	m.i	PROPN
iajs-898	136	4	.	.	PROPN
iajs-898	136	5	(	(	PUNCT
iajs-898	136	6	2002),"methods	2002),"method	NOUN
iajs-898	136	7	of	of	ADP
iajs-898	136	8	multivariate	multivariate	NOUN
iajs-898	136	9	statistics	statistic	NOUN
iajs-898	136	10	.	.	PUNCT
iajs-898	137	1	"wiley	"wiley	ADJ
iajs-898	137	2	,	,	PUNCT
iajs-898	137	3	new	new	ADJ
iajs-898	137	4	york6	york6	PROPN
iajs-898	137	5	7.hogg	7.hogg	NUM
iajs-898	137	6	,	,	PUNCT
iajs-898	137	7	r.v	r.v	PROPN
iajs-898	137	8	.	.	PROPN
iajs-898	137	9	and	and	CCONJ
iajs-898	137	10	craig	craig	PROPN
iajs-898	137	11	,	,	PUNCT
iajs-898	137	12	a.t.(1978),"introduction	a.t.(1978),"introduction	PROPN
iajs-898	137	13	to	to	ADP
iajs-898	137	14	mathematical	mathematical	ADJ
iajs-898	137	15	statistics	statistic	NOUN
iajs-898	137	16	.	.	PUNCT
iajs-898	137	17	"	"	PUNCT
iajs-898	138	1	fourth	fourth	PROPN
iajs-898	138	2	edition	edition	NOUN
iajs-898	138	3	,	,	PUNCT
iajs-898	138	4	macmillan	macmillan	PROPN
iajs-898	138	5	pub.co.new	pub.co.new	PROPN
iajs-898	138	6	york	york	PROPN
iajs-898	138	7	.	.	PUNCT
iajs-898	139	1	8.hoerl	8.hoerl	NUM
iajs-898	139	2	,	,	PUNCT
iajs-898	139	3	a.e	a.e	PROPN
iajs-898	139	4	.	.	PROPN
iajs-898	139	5	and	and	CCONJ
iajs-898	139	6	kennard	kennard	PROPN
iajs-898	139	7	,	,	PUNCT
iajs-898	139	8	r.w.(1970),"ridge	r.w.(1970),"ridge	PROPN
iajs-898	139	9	regression	regression	NOUN
iajs-898	139	10	:	:	PUNCT
iajs-898	139	11	biased	biased	ADJ
iajs-898	139	12	estimation	estimation	NOUN
iajs-898	139	13	for	for	ADP
iajs-898	139	14	non	non	ADJ
iajs-898	139	15	,	,	PUNCT
iajs-898	139	16	55_67.12orthogonal	55_67.12orthogonal	ADJ
iajs-898	139	17	problems	problem	NOUN
iajs-898	139	18	.	.	PUNCT
iajs-898	140	1	"technometrics	"technometric	NOUN
iajs-898	140	2	.	.	PUNCT
iajs-898	141	1	9.chatterjee	9.chatterjee	NUM
iajs-898	141	2	,	,	PUNCT
iajs-898	141	3	s.	s.	PROPN
iajs-898	141	4	and	and	CCONJ
iajs-898	141	5	price	price	NOUN
iajs-898	141	6	,	,	PUNCT
iajs-898	141	7	p.(1977),"regression	p.(1977),"regression	VERB
iajs-898	141	8	analysis	analysis	NOUN
iajs-898	141	9	by	by	ADP
iajs-898	141	10	example	example	NOUN
iajs-898	141	11	.	.	PUNCT
iajs-898	142	1	"john	"john	PROPN
iajs-898	142	2	wiley	wiley	PROPN
iajs-898	142	3	and	and	CCONJ
iajs-898	142	4	sons	son	NOUN
iajs-898	142	5	,	,	PUNCT
iajs-898	142	6	new	new	PROPN
iajs-898	142	7	york	york	PROPN
iajs-898	142	8	.	.	PUNCT
iajs-898	143	1	10.marquardit	10.marquardit	NUM
iajs-898	143	2	,	,	PUNCT
iajs-898	143	3	d.w.(1970),"generalized	d.w.(1970),"generalize	VERB
iajs-898	143	4	inverse	inverse	NOUN
iajs-898	143	5	,	,	PUNCT
iajs-898	143	6	ridge	ridge	NOUN
iajs-898	143	7	regression	regression	NOUN
iajs-898	143	8	,	,	PUNCT
iajs-898	143	9	and	and	CCONJ
iajs-898	143	10	nonlinear	nonlinear	ADJ
iajs-898	143	11	,	,	PUNCT
iajs-898	143	12	591_612.12estimation	591_612.12estimation	NOUN
iajs-898	143	13	.	.	PUNCT
iajs-898	144	1	"technometrics	"technometric	NOUN
iajs-898	144	2	.	.	PUNCT
iajs-898	144	3	تخطیط	تخطیط	PROPN
iajs-898	144	4	الموارد	الموارد	AUX
iajs-898	144	5	البشریة	البشریة	VERB
iajs-898	144	6	ودورھا	ودورھا	PROPN
iajs-898	144	7	في	في	PRON
iajs-898	144	8	تنمیة	تنمیة	PROPN
iajs-898	144	9	القطاع	القطاع	PROPN
iajs-898	144	10	الصناعي	الصناعي	ADJ
iajs-898	144	11	في	في	SCONJ
iajs-898	144	12	العراق	العراق	PROPN
iajs-898	144	13	)	)	PUNCT
iajs-898	144	14	:	:	PUNCT
iajs-898	144	15	2004	2004	NUM
iajs-898	144	16	.	.	PUNCT
iajs-898	145	1	(	(	PUNCT
iajs-898	145	2	خالد	خالد	NOUN
iajs-898	145	3	ابراھیم	ابراھیم	PROPN
iajs-898	145	4	سلمان	سلمان	PROPN
iajs-898	145	5	،	،	NOUN
iajs-898	145	6	الجبوري	الجبوري	VERB
iajs-898	145	7	11	11	NUM
iajs-898	145	8	.	.	PUNCT
iajs-898	146	1	رسالة	رسالة	NOUN
iajs-898	146	2	ماجستیر	ماجستیر	PROPN
iajs-898	146	3	مقدمة	مقدمة	PROPN
iajs-898	146	4	الى	الى	PROPN
iajs-898	146	5	مجلس	مجلس	PROPN
iajs-898	146	6	المعھد	المعھد	PROPN
iajs-898	146	7	العالي	العالي	PROPN
iajs-898	146	8	للدراسات	للدراسات	PROPN
iajs-898	147	1	السیاسیة	السیاسیة	PROPN
iajs-898	147	2	والدولیة	والدولیة	PROPN
iajs-898	147	3	في	في	ADP
iajs-898	147	4	الجامعة	الجامعة	NOUN
iajs-898	147	5	.	.	PUNCT
iajs-898	148	1	1990_1970للفترة	1990_1970للفترة	NUM
iajs-898	148	2	.المستنصریة	.المستنصریة	NOUN
iajs-898	148	3	ihjpas	ihjpa	VERB
iajs-898	148	4	ibn	ibn	PROPN
iajs-898	148	5	alhaitham	alhaitham	PROPN
iajs-898	148	6	j.	j.	PROPN
iajs-898	148	7	for	for	ADP
iajs-898	148	8	pure	pure	ADJ
iajs-898	148	9	&	&	CCONJ
iajs-898	148	10	appl	appl	PROPN
iajs-898	148	11	.	.	PUNCT
iajs-898	149	1	sci	sci	PROPN
iajs-898	149	2	.	.	PUNCT
iajs-898	149	3	vo	vo	PROPN
iajs-898	149	4	l.23	l.23	PROPN
iajs-898	149	5	(	(	PUNCT
iajs-898	149	6	3	3	NUM
iajs-898	149	7	)	)	PUNCT
iajs-898	149	8	2010	2010	NUM
iajs-898	149	9	table(1)effects	table(1)effect	NOUN
iajs-898	149	10	of	of	ADP
iajs-898	149	11	five	five	NUM
iajs-898	149	12	explanatory	explanatory	ADJ
iajs-898	149	13	variables	variable	NOUN
iajs-898	149	14	x1,x2,	x1,x2,	PROPN
iajs-898	149	15	…	…	PUNCT
iajs-898	149	16	.,x5	.,x5	PROPN
iajs-898	149	17	the	the	DET
iajs-898	149	18	response	response	NOUN
iajs-898	149	19	variable	variable	ADJ
iajs-898	149	20	y	y	PROPN
iajs-898	149	21	on	on	ADP
iajs-898	149	22	table(2	table(2	NOUN
iajs-898	149	23	)	)	PUNCT
iajs-898	149	24	correlation	correlation	NOUN
iajs-898	149	25	matrix	matrix	NOUN
iajs-898	149	26	section	section	NOUN
iajs-898	149	27	y	y	PROPN
iajs-898	149	28	x5	x5	PROPN
iajs-898	149	29	x4	x4	PROPN
iajs-898	150	1	x3	x3	PROPN
iajs-898	150	2	x2	x2	PROPN
iajs-898	151	1	x1	x1	NUM
iajs-898	151	2	0.749380	0.749380	NUM
iajs-898	151	3	0.821688	0.821688	NUM
iajs-898	151	4	0.380475	0.380475	NUM
iajs-898	151	5	0.780634	0.780634	NUM
iajs-898	151	6	0.967806	0.967806	NUM
iajs-898	151	7	1.000000	1.000000	NUM
iajs-898	152	1	x1	x1	PROPN
iajs-898	152	2	0.790267	0.790267	NUM
iajs-898	152	3	0.912351	0.912351	NUM
iajs-898	152	4	-0.541955	-0.541955	NOUN
iajs-898	152	5	0.688120	0.688120	NUM
iajs-898	152	6	1.000000	1.000000	NUM
iajs-898	152	7	0.967806	0.967806	NUM
iajs-898	152	8	x2	x2	PROPN
iajs-898	152	9	0.631133	0.631133	NUM
iajs-898	152	10	0.513239	0.513239	NUM
iajs-898	152	11	0.028945	0.028945	NUM
iajs-898	152	12	1.000000	1.000000	NUM
iajs-898	152	13	0.688120	0.688120	NUM
iajs-898	152	14	0.780634	0.780634	NUM
iajs-898	152	15	x3	x3	NOUN
iajs-898	152	16	0.295671	0.295671	NUM
iajs-898	153	1	0.645230	0.645230	NUM
iajs-898	153	2	1.000000	1.000000	NUM
iajs-898	153	3	0.028945	0.028945	NUM
iajs-898	153	4	0.541955	0.541955	NUM
iajs-898	153	5	0.380475	0.380475	NUM
iajs-898	153	6	x4	x4	SYM
iajs-898	153	7	0.806230	0.806230	NUM
iajs-898	153	8	1.000000	1.000000	NUM
iajs-898	153	9	0.645230	0.645230	NUM
iajs-898	153	10	0.513239	0.513239	NUM
iajs-898	153	11	0.912351	0.912351	NOUN
iajs-898	153	12	0.821688	0.821688	NUM
iajs-898	153	13	x5	x5	NOUN
iajs-898	153	14	1.000000	1.000000	NUM
iajs-898	153	15	0.806230	0.806230	NUM
iajs-898	153	16	0.295671	0.295671	NUM
iajs-898	153	17	0.631133	0.631133	NUM
iajs-898	153	18	0.790267	0.790267	NUM
iajs-898	153	19	0.749380	0.749380	NUM
iajs-898	153	20	y	y	PROPN
iajs-898	153	21	table(3	table(3	PROPN
iajs-898	153	22	)	)	PUNCT
iajs-898	153	23	eigen	eigen	PROPN
iajs-898	153	24	values	value	NOUN
iajs-898	153	25	of	of	ADP
iajs-898	153	26	correlations	correlation	NOUN
iajs-898	153	27	x5	x5	PROPN
iajs-898	153	28	x4	x4	PROPN
iajs-898	153	29	x3	x3	PROPN
iajs-898	154	1	x2	x2	PROPN
iajs-898	155	1	x1	x1	PROPN
iajs-898	155	2	y	y	PROPN
iajs-898	155	3	14216	14216	NUM
iajs-898	155	4	50096	50096	NUM
iajs-898	155	5	24524	24524	NUM
iajs-898	155	6	473	473	NUM
iajs-898	155	7	1516	1516	NUM
iajs-898	155	8	1271	1271	NUM
iajs-898	155	9	16467	16467	NUM
iajs-898	155	10	57133	57133	NUM
iajs-898	155	11	26012	26012	NUM
iajs-898	155	12	1923	1923	NUM
iajs-898	155	13	1514	1514	NUM
iajs-898	155	14	1440	1440	NUM
iajs-898	155	15	18697	18697	NUM
iajs-898	155	16	66795	66795	NUM
iajs-898	155	17	30360	30360	NUM
iajs-898	155	18	2490	2490	NUM
iajs-898	155	19	2037	2037	NUM
iajs-898	155	20	1355	1355	NUM
iajs-898	155	21	18716	18716	NUM
iajs-898	155	22	66263	66263	NUM
iajs-898	155	23	29385	29385	NUM
iajs-898	155	24	2734	2734	NUM
iajs-898	155	25	2019	2019	NUM
iajs-898	155	26	1509	1509	NUM
iajs-898	155	27	20696	20696	NUM
iajs-898	155	28	61718	61718	NUM
iajs-898	155	29	36619	36619	NUM
iajs-898	155	30	3322	3322	NUM
iajs-898	155	31	2210	2210	NUM
iajs-898	155	32	1254	1254	NUM
iajs-898	155	33	23474	23474	NUM
iajs-898	155	34	61028	61028	NUM
iajs-898	155	35	37393	37393	NUM
iajs-898	155	36	3563	3563	NUM
iajs-898	155	37	2234	2234	NUM
iajs-898	155	38	1220	1220	NUM
iajs-898	155	39	25283	25283	NUM
iajs-898	155	40	69344	69344	NUM
iajs-898	155	41	40034	40034	NUM
iajs-898	155	42	4205	4205	NUM
iajs-898	155	43	2525	2525	NUM
iajs-898	155	44	1546	1546	NUM
iajs-898	155	45	26172	26172	NUM
iajs-898	155	46	66181	66181	NUM
iajs-898	155	47	10310	10310	NUM
iajs-898	155	48	4293	4293	NUM
iajs-898	155	49	2381	2381	NUM
iajs-898	155	50	1916	1916	NUM
iajs-898	155	51	28108	28108	NUM
iajs-898	155	52	62039	62039	NUM
iajs-898	155	53	49163	49163	NUM
iajs-898	155	54	5278	5278	NUM
iajs-898	155	55	2944	2944	NUM
iajs-898	155	56	2381	2381	NUM
iajs-898	155	57	30707	30707	NUM
iajs-898	155	58	69339	69339	NUM
iajs-898	155	59	57299	57299	NUM
iajs-898	155	60	8529	8529	NUM
iajs-898	155	61	3528	3528	NUM
iajs-898	155	62	2585	2585	NUM
iajs-898	155	63	32561	32561	NUM
iajs-898	155	64	69671	69671	NUM
iajs-898	155	65	60965	60965	NUM
iajs-898	155	66	9661	9661	NUM
iajs-898	155	67	4308	4308	NUM
iajs-898	155	68	2810	2810	NUM
iajs-898	155	69	33609	33609	NUM
iajs-898	155	70	69047	69047	NUM
iajs-898	155	71	56600	56600	NUM
iajs-898	155	72	9840	9840	NUM
iajs-898	155	73	4444	4444	NUM
iajs-898	155	74	1440	1440	NUM
iajs-898	155	75	32789	32789	NUM
iajs-898	155	76	65441	65441	NUM
iajs-898	155	77	55789	55789	NUM
iajs-898	155	78	10830	10830	NUM
iajs-898	155	79	4588	4588	NUM
iajs-898	155	80	2493	2493	NUM
iajs-898	155	81	52340	52340	NUM
iajs-898	155	82	44175	44175	NUM
iajs-898	155	83	44125	44125	NUM
iajs-898	155	84	12968	12968	NUM
iajs-898	155	85	4075	4075	NUM
iajs-898	155	86	3285	3285	NUM
iajs-898	155	87	47633	47633	NUM
iajs-898	155	88	52476	52476	NUM
iajs-898	155	89	46492	46492	NUM
iajs-898	155	90	12875	12875	NUM
iajs-898	155	91	4563	4563	NUM
iajs-898	155	92	3062	3062	NUM
iajs-898	155	93	63398	63398	NUM
iajs-898	155	94	48322	48322	NUM
iajs-898	155	95	46599	46599	NUM
iajs-898	155	96	12163	12163	NUM
iajs-898	155	97	4031	4031	NUM
iajs-898	155	98	3403	3403	NUM
iajs-898	155	99	62205	62205	NUM
iajs-898	155	100	49672	49672	NUM
iajs-898	155	101	49070	49070	NUM
iajs-898	155	102	12712	12712	NUM
iajs-898	155	103	4479	4479	NUM
iajs-898	155	104	2875	2875	NUM
iajs-898	155	105	47530	47530	NUM
iajs-898	155	106	47330	47330	NUM
iajs-898	155	107	49035	49035	NUM
iajs-898	155	108	12610	12610	NUM
iajs-898	155	109	4343	4343	NUM
iajs-898	155	110	2861	2861	NUM
iajs-898	155	111	46260	46260	NUM
iajs-898	155	112	46160	46160	NUM
iajs-898	155	113	48013	48013	NUM
iajs-898	155	114	12615	12615	NUM
iajs-898	155	115	4299	4299	NUM
iajs-898	155	116	2596	2596	NUM
iajs-898	155	117	4510	4510	NUM
iajs-898	155	118	45123	45123	NUM
iajs-898	155	119	40860	40860	NUM
iajs-898	155	120	12630	12630	NUM
iajs-898	155	121	4345	4345	NUM
iajs-898	155	122	1710	1710	NUM
iajs-898	155	123	44915	44915	NUM
iajs-898	155	124	44925	44925	NUM
iajs-898	155	125	49095	49095	NUM
iajs-898	155	126	12955	12955	NUM
iajs-898	155	127	4865	4865	NUM
iajs-898	155	128	1777	1777	NUM
iajs-898	155	129	condition	condition	NOUN
iajs-898	155	130	number	number	NOUN
iajs-898	155	131	cumulative	cumulative	ADJ
iajs-898	155	132	percent	percent	NOUN
iajs-898	155	133	incremental	incremental	ADJ
iajs-898	155	134	percent	percent	NOUN
iajs-898	155	135	eigenvalue	eigenvalue	NOUN
iajs-898	155	136	no	no	NOUN
iajs-898	155	137	.	.	PROPN
iajs-898	156	1	1.00	1.00	NUM
iajs-898	156	2	72.47	72.47	NUM
iajs-898	156	3	72.47	72.47	NUM
iajs-898	156	4	3.623598	3.623598	NUM
iajs-898	156	5	1	1	NUM
iajs-898	156	6	3.45	3.45	NUM
iajs-898	156	7	93.51	93.51	NUM
iajs-898	156	8	21.04	21.04	NUM
iajs-898	156	9	1.051811	1.051811	NUM
iajs-898	156	10	2	2	NUM
iajs-898	156	11	7.151	7.151	NUM
iajs-898	156	12	97.73	97.73	NUM
iajs-898	156	13	4.22	4.22	NUM
iajs-898	156	14	0.211248	0.211248	NUM
iajs-898	156	15	3	3	NUM
iajs-898	156	16	34.83	34.83	NUM
iajs-898	156	17	99.81	99.81	NUM
iajs-898	156	18	2.08	2.08	NUM
iajs-898	156	19	0.104043	0.104043	NUM
iajs-898	156	20	4	4	NUM
iajs-898	156	21	389.65	389.65	NUM
iajs-898	156	22	100.00	100.00	NUM
iajs-898	156	23	0.19	0.19	NUM
iajs-898	156	24	0.009300	0.009300	NUM
iajs-898	156	25	5	5	NUM
iajs-898	156	26	ihjpas	ihjpa	VERB
iajs-898	156	27	ibn	ibn	PROPN
iajs-898	156	28	alhaitham	alhaitham	NOUN
iajs-898	157	1	j.	j.	PROPN
iajs-898	158	1	fo	fo	ADP
iajs-898	158	2	r	r	NOUN
iajs-898	158	3	pure	pure	ADJ
iajs-898	158	4	&	&	CCONJ
iajs-898	158	5	appl	appl	PROPN
iajs-898	158	6	.	.	PUNCT
iajs-898	159	1	sc	sc	PROPN
iajs-898	159	2	i.	i.	PROPN
iajs-898	159	3	vo	vo	PROPN
iajs-898	159	4	l.23	l.23	PROPN
iajs-898	159	5	(	(	PUNCT
iajs-898	159	6	3	3	NUM
iajs-898	159	7	)	)	PUNCT
iajs-898	159	8	2010	2010	NUM
iajs-898	159	9	table(4)eigenvectors	table(4)eigenvector	NOUN
iajs-898	159	10	of	of	ADP
iajs-898	159	11	correlations	correlation	NOUN
iajs-898	159	12	x5	x5	PROPN
iajs-898	159	13	x4	x4	PROPN
iajs-898	159	14	x3	x3	PROPN
iajs-898	159	15	x2	x2	PROPN
iajs-898	160	1	x1	x1	PROPN
iajs-898	160	2	eigenvalue	eigenvalue	PROPN
iajs-898	160	3	no	no	INTJ
iajs-898	160	4	.	.	PUNCT
iajs-898	161	1	0.487824	0.487824	NUM
iajs-898	161	2	0.304255	0.304255	NUM
iajs-898	162	1	0.284390	0.284390	NUM
iajs-898	162	2	0.518719	0.518719	NUM
iajs-898	162	3	0.502625	0.502625	NUM
iajs-898	162	4	3.623598	3.623598	NUM
iajs-898	162	5	1	1	NUM
iajs-898	162	6	0.210802	0.210802	NUM
iajs-898	162	7	0.751940	0.751940	NUM
iajs-898	162	8	0.592083	0.592083	NUM
iajs-898	163	1	0.007497	0.007497	NUM
iajs-898	163	2	-0.199033	-0.199033	NOUN
iajs-898	164	1	1.051811	1.051811	NUM
iajs-898	164	2	2	2	NUM
iajs-898	164	3	0.425416	0.425416	NUM
iajs-898	164	4	0.569859	0.569859	NUM
iajs-898	164	5	0.539120	0.539120	NUM
iajs-898	164	6	0.222736	0.222736	NUM
iajs-898	164	7	0.190854	0.190854	NUM
iajs-898	164	8	0.211248	0.211248	NUM
iajs-898	164	9	3	3	NUM
iajs-898	164	10	0.710381	0.710381	NUM
iajs-898	164	11	0.109447	0.109447	NUM
iajs-898	164	12	0.303237	0.303237	NUM
iajs-898	164	13	0.292886	0.292886	NUM
iajs-898	164	14	0.552851	0.552851	NUM
iajs-898	164	15	0.104043	0.104043	NUM
iajs-898	164	16	4	4	NUM
iajs-898	164	17	0.179037	0.179037	NUM
iajs-898	164	18	0.074329	0.074329	NUM
iajs-898	165	1	0.035429	0.035429	NUM
iajs-898	165	2	0.771674	0.771674	NUM
iajs-898	165	3	0.604718	0.604718	NUM
iajs-898	165	4	0.009300	0.009300	NUM
iajs-898	165	5	5	5	NUM
iajs-898	165	6	table(5)ridge	table(5)ridge	PROPN
iajs-898	165	7	vs.	vs.	ADP
iajs-898	165	8	least	least	ADJ
iajs-898	165	9	squares	square	NOUN
iajs-898	165	10	comparison	comparison	NOUN
iajs-898	165	11	section	section	NOUN
iajs-898	165	12	for	for	ADP
iajs-898	165	13	k	k	PROPN
iajs-898	165	14	=	=	PUNCT
iajs-898	165	15	0.125260	0.125260	NUM
iajs-898	165	16	ls	ls	ADJ
iajs-898	165	17	standad	standad	PROPN
iajs-898	165	18	error	error	PROPN
iajs-898	165	19	ridge	ridge	NOUN
iajs-898	165	20	standard	standard	PROPN
iajs-898	165	21	error	error	NOUN
iajs-898	165	22	ls	ls	PROPN
iajs-898	165	23	vif	vif	PROPN
iajs-898	165	24	ridge	ridge	PROPN
iajs-898	165	25	vif	vif	PROPN
iajs-898	165	26	standarded	standarde	VERB
iajs-898	165	27	ls	ls	ADJ
iajs-898	165	28	coefficients	coefficient	NOUN
iajs-898	165	29	standardized	standardized	ADJ
iajs-898	165	30	ridge	ridge	NOUN
iajs-898	165	31	coefficients	coefficient	NOUN
iajs-898	165	32	explanatory	explanatory	ADJ
iajs-898	165	33	variables	variable	VERB
iajs-898	165	34	0.4965	0.4965	NUM
iajs-898	165	35	0.0884	0.0884	NUM
iajs-898	165	36	42.54	42.54	NUM
iajs-898	166	1	0.955	0.955	NUM
iajs-898	166	2	0.9859	0.9859	NUM
iajs-898	167	1	0.0126	0.0126	NUM
iajs-898	168	1	x1	x1	NUM
iajs-898	168	2	0.1562	0.1562	NUM
iajs-898	168	3	0.0184	0.0184	NUM
iajs-898	168	4	65.16	65.16	NUM
iajs-898	168	5	0.637	0.637	NUM
iajs-898	168	6	1.1247	1.1247	NUM
iajs-898	168	7	0.2329	0.2329	NUM
iajs-898	168	8	x2	x2	PROPN
iajs-898	168	9	0.0127	0.0127	NUM
iajs-898	168	10	0.0093	0.0093	NUM
iajs-898	168	11	3.326	3.326	NUM
iajs-898	168	12	1.248	1.248	NUM
iajs-898	168	13	0.2634	0.2634	NUM
iajs-898	168	14	0.1695	0.1695	NUM
iajs-898	168	15	x3	x3	NUM
iajs-898	168	16	0.0154	0.0154	NUM
iajs-898	168	17	0.0114	0.0114	NUM
iajs-898	169	1	2.808	2.808	NUM
iajs-898	169	2	1.085	1.085	NUM
iajs-898	169	3	0.4680	0.4680	NUM
iajs-898	169	4	0.1653	0.1653	NUM
iajs-898	169	5	x4	x4	ADV
iajs-898	169	6	0.0181	0.0181	NUM
iajs-898	169	7	0.0085	0.0085	NUM
iajs-898	169	8	9.261	9.261	NUM
iajs-898	169	9	1.447	1.447	NUM
iajs-898	169	10	0.7006	0.7006	NUM
iajs-898	169	11	0.5359	0.5359	NUM
iajs-898	169	12	x5	x5	NOUN
iajs-898	169	13	table(6)ridge	table(6)ridge	PROPN
iajs-898	169	14	vs.	vs.	ADP
iajs-898	169	15	least	least	ADJ
iajs-898	169	16	squares	square	NOUN
iajs-898	169	17	comparison	comparison	NOUN
iajs-898	169	18	section	section	NOUN
iajs-898	169	19	for	for	ADP
iajs-898	169	20	k	k	PROPN
iajs-898	169	21	=	=	SYM
iajs-898	169	22	0.494107	0.494107	NUM
iajs-898	169	23	ls	ls	ADJ
iajs-898	169	24	standard	standard	ADJ
iajs-898	169	25	error	error	NOUN
iajs-898	169	26	ridge	ridge	NOUN
iajs-898	169	27	standard	standard	NOUN
iajs-898	169	28	error	error	NOUN
iajs-898	169	29	ls	ls	PROPN
iajs-898	169	30	vif	vif	PROPN
iajs-898	169	31	ridge	ridge	PROPN
iajs-898	169	32	vif	vif	PROPN
iajs-898	169	33	standardized	standardize	VERB
iajs-898	169	34	ls	ls	ADJ
iajs-898	169	35	coefficients	coefficient	NOUN
iajs-898	169	36	standardized	standardized	ADJ
iajs-898	169	37	ridge	ridge	NOUN
iajs-898	169	38	coefficients	coefficient	NOUN
iajs-898	169	39	explanatory	explanatory	ADJ
iajs-898	169	40	variables	variable	VERB
iajs-898	169	41	0.4965	0.4965	NUM
iajs-898	169	42	0.0447	0.0447	NUM
iajs-898	169	43	42.54	42.54	NUM
iajs-898	169	44	0.1892	0.1892	NUM
iajs-898	169	45	-0.9859	-0.9859	PUNCT
iajs-898	170	1	0.1319	0.1319	NUM
iajs-898	170	2	x1	x1	NUM
iajs-898	170	3	0.1562	0.1562	NUM
iajs-898	170	4	0.0092	0.0092	NUM
iajs-898	170	5	65.16	65.16	NUM
iajs-898	170	6	0.1254	0.1254	NUM
iajs-898	170	7	.12471	.12471	NOUN
iajs-898	171	1	0.1970	0.1970	NUM
iajs-898	171	2	x2	x2	NOUN
iajs-898	171	3	0.0127	0.0127	NUM
iajs-898	171	4	0.0059	0.0059	NUM
iajs-898	171	5	3.326	3.326	NUM
iajs-898	171	6	0.3861	0.3861	NUM
iajs-898	171	7	0.2634	0.2634	NUM
iajs-898	171	8	0.1563	0.1563	NUM
iajs-898	171	9	x3	x3	NUM
iajs-898	171	10	0.0154	0.0154	NUM
iajs-898	171	11	0.0079	0.0079	NUM
iajs-898	171	12	2.808	2.808	NUM
iajs-898	171	13	0.4101	0.4101	NUM
iajs-898	171	14	0.4680	0.4680	NUM
iajs-898	171	15	0.0452	0.0452	NUM
iajs-898	171	16	x4	x4	ADV
iajs-898	171	17	0.0181	0.0181	NUM
iajs-898	171	18	0.0044	0.0044	NUM
iajs-898	171	19	9.261	9.261	NUM
iajs-898	171	20	0.2951	0.2951	NUM
iajs-898	171	21	0.7006	0.7006	NUM
iajs-898	171	22	0.3126	0.3126	NUM
iajs-898	171	23	x5	x5	NOUN
iajs-898	171	24	ihjpas	ihjpa	VERB
iajs-898	171	25	2010	2010	NUM
iajs-898	171	26	)	)	PUNCT
iajs-898	171	27	3	3	NUM
iajs-898	171	28	(	(	PUNCT
iajs-898	171	29	23مجلة	23مجلة	NUM
iajs-898	171	30	ابن	ابن	PROPN
iajs-898	171	31	الھیثم	الھیثم	PROPN
iajs-898	171	32	للعلوم	للعلوم	PROPN
iajs-898	171	33	الصرفة	الصرفة	PROPN
iajs-898	171	34	والتطبیقیة	والتطبیقیة	PROPN
iajs-898	171	35	المجلد	المجلد	PROPN
iajs-898	172	1	توظیف	توظیف	PROPN
iajs-898	172	2	اسلوب	اسلوب	VERB
iajs-898	172	3	بیز	بیز	PROPN
iajs-898	172	4	في	في	ADP
iajs-898	172	5	تحلیل	تحلیل	VERB
iajs-898	172	6	انحدار	انحدار	PROPN
iajs-898	172	7	الحرف	الحرف	PROPN
iajs-898	172	8	حازم	حازم	PROPN
iajs-898	172	9	منصور	منصور	PROPN
iajs-898	172	10	كوركیس	كوركیس	ADJ
iajs-898	172	11	جامعة	جامعة	NOUN
iajs-898	172	12	بغداد،ابن	بغداد،ابن	PROPN
iajs-898	172	13	الھیثم	الھیثم	VERB
iajs-898	172	14	-كلیة	-كلیة	PROPN
iajs-898	172	15	التربیة،قسم	التربیة،قسم	PROPN
iajs-898	172	16	الریاضیات	الریاضیات	NOUN
iajs-898	172	17	خالصھال	خالصھال	NOUN
iajs-898	172	18	فر	فر	ADP
iajs-898	172	19	معلومات	معلومات	PROPN
iajs-898	172	20	مسبقة	مسبقة	PROPN
iajs-898	172	21	عن	عن	PROPN
iajs-898	172	22	احرف	احرف	ADJ
iajs-898	172	23	على	على	PROPN
iajs-898	172	24	فرض	فرض	PROPN
iajs-898	172	25	تواسلوب	تواسلوب	NOUN
iajs-898	172	26	بیز	بیز	NOUN
iajs-898	172	27	في	في	ADP
iajs-898	172	28	تحلیل	تحلیل	VERB
iajs-898	172	29	انحدار	انحدار	PROPN
iajs-898	172	30	الحرف	الحرف	NOUN
iajs-898	172	31	وتقدیر	وتقدیر	PROPN
iajs-898	172	32	معلمة	معلمة	NOUN
iajs-898	172	33	ال	ال	ADP
iajs-898	172	34	وظففي	وظففي	PROPN
iajs-898	172	35	ھذا	ھذا	PROPN
iajs-898	172	36	البحث	البحث	VERB
iajs-898	172	37	كما	كما	PROPN
iajs-898	172	38	تم	تم	PROPN
iajs-898	172	39	،	،	PROPN
iajs-898	172	40	وان	وان	PROPN
iajs-898	172	41	االنموذج	االنموذج	PROPN
iajs-898	172	42	یعاني	یعاني	PROPN
iajs-898	172	43	من	من	AUX
iajs-898	172	44	مشكلة	مشكلة	VERB
iajs-898	172	45	التعدد	التعدد	PROPN
iajs-898	172	46	الخطي	الخطي	PROPN
iajs-898	172	47	غیر	غیر	PROPN
iajs-898	172	48	التام	التام	PROPN
iajs-898	172	49	بدرجة	بدرجة	PROPN
iajs-898	172	50	كبیرة	كبیرة	PROPN
iajs-898	172	51	،	،	X
iajs-898	172	52	معلمات	معلمات	PROPN
iajs-898	172	53	انموذج	انموذج	PROPN
iajs-898	172	54	االنحدار	االنحدار	PROPN
iajs-898	172	55	الخطي	الخطي	PROPN
iajs-898	172	56	العام	العام	PROPN
iajs-898	172	57	ومن	ومن	PROPN
iajs-898	172	58	1970في	1970في	PROPN
iajs-898	172	59	عام	عام	NOUN
iajs-898	172	60	hoerl	hoerl	NOUN
iajs-898	172	61	and	and	CCONJ
iajs-898	172	62	kennardاستخدام	kennardاستخدام	AUX
iajs-898	172	63	اسلوب	اسلوب	VERB
iajs-898	172	64	جدید	جدید	PROPN
iajs-898	172	65	في	في	ADP
iajs-898	172	66	ایجاد	ایجاد	PROPN
iajs-898	172	67	مقدر	مقدر	PROPN
iajs-898	172	68	معلمة	معلمة	VERB
iajs-898	172	69	الحرف	الحرف	PROPN
iajs-898	172	70	الذي	الذي	PROPN
iajs-898	172	71	اقترحھ	اقترحھ	PROPN
iajs-898	173	1	كل	كل	PROPN
iajs-898	173	2	من	من	INTJ
iajs-898	174	1	.عددي	.عددي	PUNCT
iajs-898	174	2	اجریت	اجریت	PROPN
iajs-898	174	3	مقارنة	مقارنة	PROPN
iajs-898	174	4	ألفضلیة	ألفضلیة	ADJ
iajs-898	174	5	اداء	اداء	PROPN
iajs-898	174	6	ھذه	ھذه	VERB
iajs-898	174	7	المقدرات	المقدرات	PROPN
iajs-898	174	8	خالل	خالل	PROPN
iajs-898	174	9	دراسة	دراسة	PROPN
iajs-898	174	10	مثال	مثال	PROPN
iajs-898	174	11	ihjpas	ihjpa	VERB