id	sid	tid	token	lemma	pos
m-392	1	1	adequacy	adequacy	NOUN
m-392	1	2	of	of	ADP
m-392	1	3	h	h	NOUN
m-392	1	4	-	-	PUNCT
m-392	1	5	likelihood	likelihood	NOUN
m-392	1	6	estimation	estimation	NOUN
m-392	1	7	method	method	NOUN
m-392	1	8	for	for	ADP
m-392	1	9	unbalanced	unbalanced	ADJ
m-392	1	10	clustered	clustered	ADJ
m-392	1	11	counting	counting	NOUN
m-392	1	12	data	datum	NOUN
m-392	1	13	models	model	NOUN
m-392	1	14	.	.	PUNCT
m-392	2	1	intesar	intesar	PROPN
m-392	2	2	n.	n.	PROPN
m-392	2	3	el	el	PROPN
m-392	2	4	-	-	PUNCT
m-392	2	5	saeiti1	saeiti1	PROPN
m-392	2	6	,	,	PUNCT
m-392	2	7	khalil	khalil	PROPN
m-392	2	8	mostafa	mostafa	PROPN
m-392	2	9	alsawi2	alsawi2	PROPN
m-392	2	10	and	and	CCONJ
m-392	2	11	gebriel	gebriel	ADJ
m-392	2	12	m.	m.	NOUN
m-392	3	1	shamia3	shamia3	PROPN
m-392	3	2	(	(	PUNCT
m-392	3	3	1	1	X
m-392	3	4	)	)	PUNCT
m-392	3	5	assistant	assistant	NOUN
m-392	3	6	professor	professor	NOUN
m-392	3	7	,	,	PUNCT
m-392	3	8	statistics	statistics	PROPN
m-392	3	9	department	department	PROPN
m-392	3	10	,	,	PUNCT
m-392	3	11	faculty	faculty	NOUN
m-392	3	12	of	of	ADP
m-392	3	13	science	science	NOUN
m-392	3	14	,	,	PUNCT
m-392	3	15	university	university	NOUN
m-392	3	16	of	of	ADP
m-392	3	17	benghazi	benghazi	NOUN
m-392	3	18	(	(	PUNCT
m-392	3	19	2	2	X
m-392	3	20	)	)	PUNCT
m-392	3	21	a	a	DET
m-392	3	22	teacher	teacher	NOUN
m-392	3	23	at	at	ADP
m-392	3	24	a	a	DET
m-392	3	25	secondary	secondary	ADJ
m-392	3	26	school	school	NOUN
m-392	3	27	,	,	PUNCT
m-392	3	28	benghazi	benghazi	NOUN
m-392	3	29	-	-	PUNCT
m-392	3	30	libya	libya	PROPN
m-392	3	31	(	(	PUNCT
m-392	3	32	3	3	NUM
m-392	3	33	)	)	PUNCT
m-392	3	34	professor	professor	NOUN
m-392	3	35	,	,	PUNCT
m-392	3	36	statistics	statistics	PROPN
m-392	3	37	department	department	PROPN
m-392	3	38	,	,	PUNCT
m-392	3	39	faculty	faculty	NOUN
m-392	3	40	of	of	ADP
m-392	3	41	science	science	NOUN
m-392	3	42	,	,	PUNCT
m-392	3	43	university	university	NOUN
m-392	3	44	of	of	ADP
m-392	3	45	benghazi	benghazi	NOUN
m-392	3	46	correspondence	correspondence	NOUN
m-392	3	47	to	to	PART
m-392	3	48	intesar	intesar	VERB
m-392	3	49	n.	n.	PROPN
m-392	3	50	el	el	PROPN
m-392	3	51	-	-	PROPN
m-392	3	52	saeiti	saeiti	PROPN
m-392	3	53	,	,	PUNCT
m-392	3	54	entesar.el-saeiti@uob.edu.ly	entesar.el-saeiti@uob.edu.ly	PROPN
m-392	3	55	abstract	abstract	VERB
m-392	3	56	this	this	DET
m-392	3	57	article	article	NOUN
m-392	3	58	would	would	AUX
m-392	3	59	concentrate	concentrate	VERB
m-392	3	60	on	on	ADP
m-392	3	61	hierarchical	hierarchical	ADJ
m-392	3	62	generalized	generalized	ADJ
m-392	3	63	linear	linear	ADJ
m-392	3	64	models	model	NOUN
m-392	3	65	,	,	PUNCT
m-392	3	66	including	include	VERB
m-392	3	67	generalized	generalize	VERB
m-392	3	68	linear	linear	ADJ
m-392	3	69	mixed	mix	VERB
m-392	3	70	-	-	PUNCT
m-392	3	71	models	model	NOUN
m-392	3	72	,	,	PUNCT
m-392	3	73	which	which	PRON
m-392	3	74	are	be	AUX
m-392	3	75	the	the	DET
m-392	3	76	extension	extension	NOUN
m-392	3	77	of	of	ADP
m-392	3	78	linear	linear	PROPN
m-392	3	79	models	model	NOUN
m-392	3	80	.	.	PUNCT
m-392	4	1	in	in	ADP
m-392	4	2	generalized	generalized	ADJ
m-392	4	3	linear	linear	NOUN
m-392	4	4	models	model	NOUN
m-392	4	5	,	,	PUNCT
m-392	4	6	the	the	DET
m-392	4	7	dependent	dependent	ADJ
m-392	4	8	variable	variable	NOUN
m-392	4	9	assumes	assume	VERB
m-392	4	10	every	every	DET
m-392	4	11	distribution	distribution	NOUN
m-392	4	12	from	from	ADP
m-392	4	13	exponential	exponential	ADJ
m-392	4	14	family	family	NOUN
m-392	4	15	distributions	distribution	NOUN
m-392	4	16	,	,	PUNCT
m-392	4	17	e.g.	e.g.	ADV
m-392	4	18	,	,	PUNCT
m-392	4	19	normal	normal	ADJ
m-392	4	20	,	,	PUNCT
m-392	4	21	poisson	poisson	NOUN
m-392	4	22	,	,	PUNCT
m-392	4	23	binomial	binomial	ADJ
m-392	4	24	,	,	PUNCT
m-392	4	25	gamma	gamma	NOUN
m-392	4	26	,	,	PUNCT
m-392	4	27	etc	etc	X
m-392	4	28	.	.	X
m-392	5	1	the	the	DET
m-392	5	2	poisson	poisson	PROPN
m-392	5	3	-	-	PUNCT
m-392	5	4	gamma	gamma	PROPN
m-392	5	5	method	method	NOUN
m-392	5	6	was	be	AUX
m-392	5	7	applied	apply	VERB
m-392	5	8	,	,	PUNCT
m-392	5	9	where	where	SCONJ
m-392	5	10	the	the	DET
m-392	5	11	dependent	dependent	ADJ
m-392	5	12	variable	variable	NOUN
m-392	5	13	represents	represent	VERB
m-392	5	14	the	the	DET
m-392	5	15	poisson	poisson	NOUN
m-392	5	16	distribution	distribution	NOUN
m-392	5	17	and	and	CCONJ
m-392	5	18	the	the	DET
m-392	5	19	standard	standard	ADJ
m-392	5	20	error	error	NOUN
m-392	5	21	is	be	AUX
m-392	5	22	defined	define	VERB
m-392	5	23	by	by	ADP
m-392	5	24	the	the	DET
m-392	5	25	gamma	gamma	NOUN
m-392	5	26	distribution	distribution	NOUN
m-392	5	27	.	.	PUNCT
m-392	6	1	in	in	ADP
m-392	6	2	generalized	generalized	ADJ
m-392	6	3	linear	linear	ADJ
m-392	6	4	models	model	NOUN
m-392	6	5	,	,	PUNCT
m-392	6	6	several	several	ADJ
m-392	6	7	estimation	estimation	NOUN
m-392	6	8	methods	method	NOUN
m-392	6	9	have	have	AUX
m-392	6	10	been	be	AUX
m-392	6	11	used	use	VERB
m-392	6	12	.	.	PUNCT
m-392	7	1	throughout	throughout	ADP
m-392	7	2	this	this	DET
m-392	7	3	study	study	NOUN
m-392	7	4	,	,	PUNCT
m-392	7	5	the	the	DET
m-392	7	6	hierarchical	hierarchical	ADJ
m-392	7	7	likelihood	likelihood	NOUN
m-392	7	8	estimation	estimation	NOUN
m-392	7	9	method	method	NOUN
m-392	7	10	was	be	AUX
m-392	7	11	used	use	VERB
m-392	7	12	to	to	PART
m-392	7	13	determine	determine	VERB
m-392	7	14	the	the	DET
m-392	7	15	effectiveness	effectiveness	NOUN
m-392	7	16	of	of	ADP
m-392	7	17	this	this	DET
m-392	7	18	methodology	methodology	NOUN
m-392	7	19	for	for	ADP
m-392	7	20	both	both	DET
m-392	7	21	data	datum	NOUN
m-392	7	22	balanced	balanced	ADJ
m-392	7	23	and	and	CCONJ
m-392	7	24	unbalanced	unbalanced	ADJ
m-392	7	25	.	.	PUNCT
m-392	8	1	this	this	DET
m-392	8	2	article	article	NOUN
m-392	8	3	compares	compare	VERB
m-392	8	4	the	the	DET
m-392	8	5	adequacy	adequacy	NOUN
m-392	8	6	of	of	ADP
m-392	8	7	poisson	poisson	PROPN
m-392	8	8	-	-	PROPN
m-392	8	9	gamma	gamma	NOUN
m-392	8	10	h	h	NOUN
m-392	8	11	-	-	PUNCT
m-392	8	12	likelihood	likelihood	NOUN
m-392	8	13	estimation	estimation	NOUN
m-392	8	14	method	method	NOUN
m-392	8	15	of	of	ADP
m-392	8	16	mixed	mixed	ADJ
m-392	8	17	effects	effect	NOUN
m-392	8	18	clustered	cluster	VERB
m-392	8	19	data	data	NOUN
m-392	8	20	models	model	NOUN
m-392	8	21	with	with	ADP
m-392	8	22	equal	equal	ADJ
m-392	8	23	and	and	CCONJ
m-392	8	24	unequal	unequal	ADJ
m-392	8	25	cluster	cluster	NOUN
m-392	8	26	sizes	size	NOUN
m-392	8	27	.	.	PUNCT
m-392	9	1	this	this	PRON
m-392	9	2	was	be	AUX
m-392	9	3	evaluated	evaluate	VERB
m-392	9	4	in	in	ADP
m-392	9	5	terms	term	NOUN
m-392	9	6	of	of	ADP
m-392	9	7	probability	probability	NOUN
m-392	9	8	of	of	ADP
m-392	9	9	type	type	NOUN
m-392	9	10	-	-	PUNCT
m-392	9	11	i	i	NOUN
m-392	9	12	error	error	NOUN
m-392	9	13	rate	rate	NOUN
m-392	9	14	,	,	PUNCT
m-392	9	15	power	power	NOUN
m-392	9	16	and	and	CCONJ
m-392	9	17	standard	standard	ADJ
m-392	9	18	error	error	NOUN
m-392	9	19	by	by	ADP
m-392	9	20	applying	apply	VERB
m-392	9	21	computer	computer	NOUN
m-392	9	22	simulation	simulation	NOUN
m-392	9	23	.	.	PUNCT
m-392	10	1	simulation	simulation	NOUN
m-392	10	2	is	be	AUX
m-392	10	3	performed	perform	VERB
m-392	10	4	using	use	VERB
m-392	10	5	different	different	ADJ
m-392	10	6	cluster	cluster	NOUN
m-392	10	7	numbers	number	NOUN
m-392	10	8	and	and	CCONJ
m-392	10	9	different	different	ADJ
m-392	10	10	cluster	cluster	NOUN
m-392	10	11	sizes	size	NOUN
m-392	10	12	.	.	PUNCT
m-392	11	1	the	the	DET
m-392	11	2	results	result	NOUN
m-392	11	3	show	show	VERB
m-392	11	4	that	that	SCONJ
m-392	11	5	the	the	DET
m-392	11	6	performance	performance	NOUN
m-392	11	7	of	of	ADP
m-392	11	8	the	the	DET
m-392	11	9	hierarchical	hierarchical	ADJ
m-392	11	10	likelihood	likelihood	NOUN
m-392	11	11	estimation	estimation	NOUN
m-392	11	12	technique	technique	NOUN
m-392	11	13	provided	provide	VERB
m-392	11	14	close	close	ADJ
m-392	11	15	approximations	approximation	NOUN
m-392	11	16	in	in	ADP
m-392	11	17	the	the	DET
m-392	11	18	event	event	NOUN
m-392	11	19	of	of	ADP
m-392	11	20	balanced	balanced	ADJ
m-392	11	21	and	and	CCONJ
m-392	11	22	unbalanced	unbalanced	ADJ
m-392	11	23	data	datum	NOUN
m-392	11	24	,	,	PUNCT
m-392	11	25	while	while	SCONJ
m-392	11	26	the	the	DET
m-392	11	27	output	output	NOUN
m-392	11	28	of	of	ADP
m-392	11	29	the	the	DET
m-392	11	30	technique	technique	NOUN
m-392	11	31	was	be	AUX
m-392	11	32	approximately	approximately	ADV
m-392	11	33	equivalent	equivalent	ADJ
m-392	11	34	in	in	ADP
m-392	11	35	both	both	DET
m-392	11	36	instances	instance	NOUN
m-392	11	37	,	,	PUNCT
m-392	11	38	regardless	regardless	ADV
m-392	11	39	of	of	ADP
m-392	11	40	cluster	cluster	NOUN
m-392	11	41	size	size	NOUN
m-392	11	42	inequality	inequality	NOUN
m-392	11	43	.	.	PUNCT
m-392	12	1	keywords	keyword	NOUN
m-392	12	2	:	:	PUNCT
m-392	12	3	hierarchical	hierarchical	ADJ
m-392	12	4	generalized	generalized	ADJ
m-392	12	5	linear	linear	ADJ
m-392	12	6	model	model	NOUN
m-392	12	7	(	(	PUNCT
m-392	12	8	hglm	hglm	NOUN
m-392	12	9	)	)	PUNCT
m-392	12	10	,	,	PUNCT
m-392	12	11	poisson	poisson	PROPN
m-392	12	12	-	-	PUNCT
m-392	12	13	gamma	gamma	NOUN
m-392	12	14	h	h	NOUN
m-392	12	15	-likelihood	-likelihood	PROPN
m-392	12	16	,	,	PUNCT
m-392	12	17	counting	count	VERB
m-392	12	18	response	response	NOUN
m-392	12	19	,	,	PUNCT
m-392	12	20	balanced	balanced	ADJ
m-392	12	21	clustered	cluster	VERB
m-392	12	22	,	,	PUNCT
m-392	12	23	unbalanced	unbalanced	ADJ
m-392	12	24	cluster	cluster	NOUN
m-392	12	25	.	.	PUNCT
m-392	13	1	introduction	introduction	NOUN
m-392	13	2	linear	linear	PROPN
m-392	13	3	models	model	NOUN
m-392	13	4	define	define	VERB
m-392	13	5	a	a	DET
m-392	13	6	continuous	continuous	ADJ
m-392	13	7	response	response	NOUN
m-392	13	8	variable	variable	NOUN
m-392	13	9	as	as	ADP
m-392	13	10	a	a	DET
m-392	13	11	function	function	NOUN
m-392	13	12	of	of	ADP
m-392	13	13	one	one	NUM
m-392	13	14	or	or	CCONJ
m-392	13	15	more	more	ADJ
m-392	13	16	predictor	predictor	NOUN
m-392	13	17	variables	variable	NOUN
m-392	13	18	.	.	PUNCT
m-392	14	1	they	they	PRON
m-392	14	2	may	may	AUX
m-392	14	3	help	help	VERB
m-392	14	4	you	you	PRON
m-392	14	5	understand	understand	VERB
m-392	14	6	and	and	CCONJ
m-392	14	7	predict	predict	VERB
m-392	14	8	the	the	DET
m-392	14	9	behavior	behavior	NOUN
m-392	14	10	of	of	ADP
m-392	14	11	complex	complex	ADJ
m-392	14	12	systems	system	NOUN
m-392	14	13	or	or	CCONJ
m-392	14	14	analyze	analyze	VERB
m-392	14	15	experimental	experimental	ADJ
m-392	14	16	,	,	PUNCT
m-392	14	17	financial	financial	ADJ
m-392	14	18	and	and	CCONJ
m-392	14	19	biological	biological	ADJ
m-392	14	20	data	datum	NOUN
m-392	14	21	.	.	PUNCT
m-392	15	1	linear	linear	ADJ
m-392	15	2	regression	regression	NOUN
m-392	15	3	is	be	AUX
m-392	15	4	a	a	DET
m-392	15	5	statistical	statistical	ADJ
m-392	15	6	method	method	NOUN
m-392	15	7	used	use	VERB
m-392	15	8	to	to	PART
m-392	15	9	construct	construct	VERB
m-392	15	10	a	a	DET
m-392	15	11	linear	linear	ADJ
m-392	15	12	model	model	NOUN
m-392	15	13	.	.	PUNCT
m-392	16	1	the	the	DET
m-392	16	2	model	model	NOUN
m-392	16	3	describes	describe	VERB
m-392	16	4	the	the	DET
m-392	16	5	relationship	relationship	NOUN
m-392	16	6	between	between	ADP
m-392	16	7	the	the	DET
m-392	16	8	dependent	dependent	ADJ
m-392	16	9	variable	variable	ADJ
m-392	16	10	y	y	NOUN
m-392	16	11	(	(	PUNCT
m-392	16	12	also	also	ADV
m-392	16	13	known	know	VERB
m-392	16	14	as	as	ADP
m-392	16	15	the	the	DET
m-392	16	16	response	response	NOUN
m-392	16	17	)	)	PUNCT
m-392	16	18	,	,	PUNCT
m-392	16	19	as	as	ADP
m-392	16	20	a	a	DET
m-392	16	21	function	function	NOUN
m-392	16	22	of	of	ADP
m-392	16	23	one	one	NUM
m-392	16	24	or	or	CCONJ
m-392	16	25	more	more	ADV
m-392	16	26	independent	independent	ADJ
m-392	16	27	x	x	ADJ
m-392	16	28	variables	variable	NOUN
m-392	16	29	(	(	PUNCT
m-392	16	30	called	call	VERB
m-392	16	31	predictors	predictor	NOUN
m-392	16	32	)	)	PUNCT
m-392	16	33	.	.	PUNCT
m-392	17	1	y	y	PROPN
m-392	17	2	=	=	PRON
m-392	17	3	xβ	xβ	PROPN
m-392	17	4	+	+	NUM
m-392	17	5	�	�	PROPN
m-392	17	6	,	,	PUNCT
m-392	17	7	…	…	PUNCT
m-392	17	8	(	(	PUNCT
m-392	17	9	1	1	NUM
m-392	17	10	)	)	PUNCT
m-392	17	11	where	where	SCONJ
m-392	17	12	β	β	NOUN
m-392	17	13	represents	represent	VERB
m-392	17	14	linear	linear	ADJ
m-392	17	15	parameter	parameter	NOUN
m-392	17	16	estimates	estimate	NOUN
m-392	17	17	to	to	PART
m-392	17	18	be	be	AUX
m-392	17	19	evaluated	evaluate	VERB
m-392	17	20	and	and	CCONJ
m-392	17	21	�	�	PROPN
m-392	17	22	represents	represent	VERB
m-392	17	23	the	the	DET
m-392	17	24	error	error	NOUN
m-392	17	25	terms	term	NOUN
m-392	17	26	.	.	PUNCT
m-392	18	1	ijo	ijo	PROPN
m-392	18	2	international	international	PROPN
m-392	18	3	journal	journal	PROPN
m-392	18	4	of	of	ADP
m-392	18	5	mathematics	mathematics	PROPN
m-392	18	6	volume	volume	PROPN
m-392	18	7	3|	3|	NUM
m-392	18	8	issue	issue	NOUN
m-392	18	9	12|	12|	NUM
m-392	18	10	december	december	PROPN
m-392	18	11	|	|	NOUN
m-392	18	12	2020	2020	NUM
m-392	18	13	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	18	14	18	18	NUM
m-392	18	15	mailto:entesar.el-saeiti@uob.edu.ly	mailto:entesar.el-saeiti@uob.edu.ly	NOUN
m-392	18	16	the	the	DET
m-392	18	17	generalized	generalize	VERB
m-392	18	18	linear	linear	PROPN
m-392	18	19	model	model	NOUN
m-392	18	20	(	(	PUNCT
m-392	18	21	glm	glm	PROPN
m-392	18	22	)	)	PUNCT
m-392	18	23	is	be	AUX
m-392	18	24	an	an	DET
m-392	18	25	extension	extension	NOUN
m-392	18	26	of	of	ADP
m-392	18	27	the	the	DET
m-392	18	28	linear	linear	ADJ
m-392	18	29	model	model	NOUN
m-392	18	30	to	to	PART
m-392	18	31	response	response	VERB
m-392	18	32	variable	variable	NOUN
m-392	18	33	that	that	PRON
m-392	18	34	follow	follow	VERB
m-392	18	35	any	any	DET
m-392	18	36	probability	probability	NOUN
m-392	18	37	distribution	distribution	NOUN
m-392	18	38	include	include	VERB
m-392	18	39	the	the	DET
m-392	18	40	exponential	exponential	ADJ
m-392	18	41	group	group	NOUN
m-392	18	42	of	of	ADP
m-392	18	43	distributions	distribution	NOUN
m-392	18	44	.	.	PUNCT
m-392	19	1	the	the	DET
m-392	19	2	exponential	exponential	ADJ
m-392	19	3	family	family	NOUN
m-392	19	4	includes	include	VERB
m-392	19	5	useful	useful	ADJ
m-392	19	6	distributions	distribution	NOUN
m-392	19	7	,	,	PUNCT
m-392	19	8	for	for	ADP
m-392	19	9	example	example	NOUN
m-392	19	10	,	,	PUNCT
m-392	19	11	normal	normal	ADJ
m-392	19	12	,	,	PUNCT
m-392	19	13	binomial	binomial	ADJ
m-392	19	14	,	,	PUNCT
m-392	19	15	poisson	poisson	NOUN
m-392	19	16	,	,	PUNCT
m-392	19	17	polynomial	polynomial	ADJ
m-392	19	18	,	,	PUNCT
m-392	19	19	gamma	gamma	NOUN
m-392	19	20	,	,	PUNCT
m-392	19	21	and	and	CCONJ
m-392	19	22	others	other	NOUN
m-392	19	23	(	(	PUNCT
m-392	19	24	leee	leee	PROPN
m-392	19	25	and	and	CCONJ
m-392	19	26	nelder	nelder	NOUN
m-392	19	27	,	,	PUNCT
m-392	19	28	2006	2006	NUM
m-392	19	29	)	)	PUNCT
m-392	19	30	.	.	PUNCT
m-392	20	1	hypothesis	hypothesis	NOUN
m-392	20	2	tests	test	NOUN
m-392	20	3	applied	apply	VERB
m-392	20	4	to	to	ADP
m-392	20	5	the	the	DET
m-392	20	6	generalized	generalize	VERB
m-392	20	7	linear	linear	PROPN
m-392	20	8	model	model	NOUN
m-392	20	9	do	do	AUX
m-392	20	10	not	not	PART
m-392	20	11	require	require	VERB
m-392	20	12	normality	normality	NOUN
m-392	20	13	of	of	ADP
m-392	20	14	the	the	DET
m-392	20	15	response	response	NOUN
m-392	20	16	variable	variable	ADJ
m-392	20	17	nor	nor	CCONJ
m-392	20	18	do	do	AUX
m-392	20	19	they	they	PRON
m-392	20	20	require	require	VERB
m-392	20	21	homogeneity	homogeneity	NOUN
m-392	20	22	of	of	ADP
m-392	20	23	variances	variance	NOUN
m-392	20	24	.	.	PUNCT
m-392	21	1	hence	hence	ADV
m-392	21	2	,	,	PUNCT
m-392	21	3	generalized	generalized	ADJ
m-392	21	4	linear	linear	NOUN
m-392	21	5	models	model	NOUN
m-392	21	6	can	can	AUX
m-392	21	7	be	be	AUX
m-392	21	8	used	use	VERB
m-392	21	9	when	when	SCONJ
m-392	21	10	response	response	NOUN
m-392	21	11	variables	variable	NOUN
m-392	21	12	follow	follow	VERB
m-392	21	13	distributions	distribution	NOUN
m-392	21	14	other	other	ADJ
m-392	21	15	than	than	ADP
m-392	21	16	the	the	DET
m-392	21	17	normal	normal	ADJ
m-392	21	18	distribution	distribution	NOUN
m-392	21	19	and	and	CCONJ
m-392	21	20	when	when	SCONJ
m-392	21	21	variances	variance	NOUN
m-392	21	22	are	be	AUX
m-392	21	23	not	not	PART
m-392	21	24	constant	constant	ADJ
m-392	21	25	.	.	PUNCT
m-392	22	1	for	for	ADP
m-392	22	2	example	example	NOUN
m-392	22	3	,	,	PUNCT
m-392	22	4	counting	count	VERB
m-392	22	5	data	datum	NOUN
m-392	22	6	would	would	AUX
m-392	22	7	be	be	AUX
m-392	22	8	appropriately	appropriately	ADV
m-392	22	9	analyzed	analyze	VERB
m-392	22	10	as	as	ADP
m-392	22	11	a	a	DET
m-392	22	12	poisson	poisson	NOUN
m-392	22	13	random	random	ADJ
m-392	22	14	variable	variable	NOUN
m-392	22	15	within	within	ADP
m-392	22	16	the	the	DET
m-392	22	17	context	context	NOUN
m-392	22	18	of	of	ADP
m-392	22	19	the	the	DET
m-392	22	20	generalized	generalized	ADJ
m-392	22	21	linear	linear	PROPN
m-392	22	22	model	model	NOUN
m-392	22	23	.	.	PUNCT
m-392	23	1	the	the	DET
m-392	23	2	generalized	generalize	VERB
m-392	23	3	linear	linear	ADJ
m-392	23	4	mixed	mixed	ADJ
m-392	23	5	model	model	NOUN
m-392	23	6	(	(	PUNCT
m-392	23	7	glmm	glmm	NOUN
m-392	23	8	)	)	PUNCT
m-392	23	9	is	be	AUX
m-392	23	10	name	name	NOUN
m-392	23	11	as	as	ADP
m-392	23	12	hierarchical	hierarchical	ADJ
m-392	23	13	generalized	generalized	ADJ
m-392	23	14	linear	linear	PROPN
m-392	23	15	model	model	NOUN
m-392	23	16	.	.	PUNCT
m-392	24	1	glmms	glmms	PROPN
m-392	24	2	can	can	AUX
m-392	24	3	be	be	AUX
m-392	24	4	thought	think	VERB
m-392	24	5	of	of	ADP
m-392	24	6	as	as	ADP
m-392	24	7	an	an	DET
m-392	24	8	extension	extension	NOUN
m-392	24	9	of	of	ADP
m-392	24	10	generalized	generalized	ADJ
m-392	24	11	linear	linear	NOUN
m-392	24	12	models	model	NOUN
m-392	24	13	(	(	PUNCT
m-392	24	14	lee	lee	PROPN
m-392	24	15	and	and	CCONJ
m-392	24	16	nelder	nelder	NOUN
m-392	24	17	,	,	PUNCT
m-392	24	18	2006	2006	NUM
m-392	24	19	)	)	PUNCT
m-392	24	20	,	,	PUNCT
m-392	24	21	the	the	DET
m-392	24	22	general	general	ADJ
m-392	24	23	form	form	NOUN
m-392	24	24	of	of	ADP
m-392	24	25	the	the	DET
m-392	24	26	model	model	NOUN
m-392	24	27	in	in	ADP
m-392	24	28	matrix	matrix	NOUN
m-392	24	29	notation	notation	NOUN
m-392	24	30	is	be	AUX
m-392	24	31	giving	give	VERB
m-392	24	32	by	by	ADP
m-392	24	33	.	.	PUNCT
m-392	24	34	�	�	PROPN
m-392	24	35	=	=	SYM
m-392	24	36	�	�	PROPN
m-392	24	37	�	�	PROPN
m-392	24	38	+	+	CCONJ
m-392	24	39	�	�	PROPN
m-392	24	40	�	�	PROPN
m-392	24	41	+	+	CCONJ
m-392	24	42	�	�	PROPN
m-392	24	43	(	(	PUNCT
m-392	24	44	2	2	NUM
m-392	24	45	)	)	PUNCT
m-392	24	46	mcculloch	mcculloch	NOUN
m-392	24	47	and	and	CCONJ
m-392	24	48	searle	searle	PROPN
m-392	24	49	(	(	PUNCT
m-392	24	50	2001	2001	NUM
m-392	24	51	)	)	PUNCT
m-392	24	52	wrote	write	VERB
m-392	24	53	,	,	PUNCT
m-392	24	54	when	when	SCONJ
m-392	24	55	studying	study	VERB
m-392	24	56	phenomena	phenomenon	NOUN
m-392	24	57	within	within	ADP
m-392	24	58	a	a	DET
m-392	24	59	given	give	VERB
m-392	24	60	period	period	NOUN
m-392	24	61	of	of	ADP
m-392	24	62	time	time	NOUN
m-392	24	63	or	or	CCONJ
m-392	24	64	area	area	NOUN
m-392	24	65	,	,	PUNCT
m-392	24	66	the	the	DET
m-392	24	67	data	datum	NOUN
m-392	24	68	of	of	ADP
m-392	24	69	any	any	DET
m-392	24	70	phenomenon	phenomenon	NOUN
m-392	24	71	will	will	AUX
m-392	24	72	follow	follow	VERB
m-392	24	73	the	the	DET
m-392	24	74	poisson	poisson	NOUN
m-392	24	75	distribution	distribution	NOUN
m-392	24	76	and	and	CCONJ
m-392	24	77	it	it	PRON
m-392	24	78	is	be	AUX
m-392	24	79	in	in	ADP
m-392	24	80	exponential	exponential	ADJ
m-392	24	81	family	family	NOUN
m-392	24	82	.	.	PUNCT
m-392	25	1	in	in	ADP
m-392	25	2	our	our	PRON
m-392	25	3	paper	paper	NOUN
m-392	25	4	,	,	PUNCT
m-392	25	5	it	it	PRON
m-392	25	6	is	be	AUX
m-392	25	7	assumed	assume	VERB
m-392	25	8	that	that	SCONJ
m-392	25	9	the	the	DET
m-392	25	10	data	datum	NOUN
m-392	25	11	follow	follow	VERB
m-392	25	12	the	the	DET
m-392	25	13	poisson	poisson	NOUN
m-392	25	14	distribution	distribution	NOUN
m-392	25	15	and	and	CCONJ
m-392	25	16	the	the	DET
m-392	25	17	error	error	NOUN
m-392	25	18	unit	unit	NOUN
m-392	25	19	follows	follow	VERB
m-392	25	20	the	the	DET
m-392	25	21	gamma	gamma	NOUN
m-392	25	22	distribution	distribution	NOUN
m-392	25	23	.	.	PUNCT
m-392	26	1	from	from	ADP
m-392	26	2	lee	lee	PROPN
m-392	26	3	and	and	CCONJ
m-392	26	4	nelder	nelder	PROPN
m-392	26	5	's	's	PART
m-392	26	6	(	(	PUNCT
m-392	26	7	1996	1996	NUM
m-392	26	8	)	)	PUNCT
m-392	26	9	description	description	NOUN
m-392	26	10	of	of	ADP
m-392	26	11	hierarchical	hierarchical	ADJ
m-392	26	12	models	model	NOUN
m-392	26	13	,	,	PUNCT
m-392	26	14	every	every	DET
m-392	26	15	distribution	distribution	NOUN
m-392	26	16	in	in	ADP
m-392	26	17	the	the	DET
m-392	26	18	exponential	exponential	ADJ
m-392	26	19	family	family	NOUN
m-392	26	20	has	have	VERB
m-392	26	21	the	the	DET
m-392	26	22	corresponding	corresponding	ADJ
m-392	26	23	distribution	distribution	NOUN
m-392	26	24	,	,	PUNCT
m-392	26	25	e.g.	e.g.	ADV
m-392	26	26	poisson	poisson	NOUN
m-392	26	27	offset	offset	VERB
m-392	26	28	by	by	ADP
m-392	26	29	gamma	gamma	NOUN
m-392	26	30	distribution	distribution	NOUN
m-392	26	31	,	,	PUNCT
m-392	26	32	binomial	binomial	ADJ
m-392	26	33	distribution	distribution	NOUN
m-392	26	34	offset	offset	VERB
m-392	26	35	by	by	ADP
m-392	26	36	beta	beta	ADJ
m-392	26	37	distribution	distribution	NOUN
m-392	26	38	,	,	PUNCT
m-392	26	39	normal	normal	ADJ
m-392	26	40	distribution	distribution	NOUN
m-392	26	41	offset	offset	VERB
m-392	26	42	by	by	ADP
m-392	26	43	normal	normal	ADJ
m-392	26	44	distribution	distribution	NOUN
m-392	26	45	.	.	PUNCT
m-392	27	1	for	for	ADP
m-392	27	2	more	more	ADJ
m-392	27	3	information	information	NOUN
m-392	27	4	on	on	ADP
m-392	27	5	hierarchical	hierarchical	ADJ
m-392	27	6	data	datum	NOUN
m-392	27	7	structure	structure	NOUN
m-392	27	8	see	see	VERB
m-392	27	9	elsaeiti	elsaeiti	ADJ
m-392	27	10	(	(	PUNCT
m-392	27	11	2013	2013	NUM
m-392	27	12	,	,	PUNCT
m-392	27	13	2014	2014	NUM
m-392	27	14	)	)	PUNCT
m-392	27	15	,	,	PUNCT
m-392	27	16	lalonde	lalonde	NOUN
m-392	27	17	(	(	PUNCT
m-392	27	18	2009	2009	NUM
m-392	27	19	)	)	PUNCT
m-392	27	20	.	.	PUNCT
m-392	28	1	cluster	cluster	NOUN
m-392	28	2	data	datum	NOUN
m-392	28	3	models	model	NOUN
m-392	28	4	are	be	AUX
m-392	28	5	frequently	frequently	ADV
m-392	28	6	used	use	VERB
m-392	28	7	in	in	ADP
m-392	28	8	the	the	DET
m-392	28	9	field	field	NOUN
m-392	28	10	of	of	ADP
m-392	28	11	agricultural	agricultural	ADJ
m-392	28	12	,	,	PUNCT
m-392	28	13	genetic	genetic	ADJ
m-392	28	14	,	,	PUNCT
m-392	28	15	industrial	industrial	ADJ
m-392	28	16	,	,	PUNCT
m-392	28	17	medical	medical	ADJ
m-392	28	18	,	,	PUNCT
m-392	28	19	biological	biological	ADJ
m-392	28	20	and	and	CCONJ
m-392	28	21	even	even	ADV
m-392	28	22	social	social	ADJ
m-392	28	23	science	science	NOUN
m-392	28	24	experiments	experiment	NOUN
m-392	28	25	.	.	PUNCT
m-392	29	1	clustered	clustered	ADJ
m-392	29	2	data	datum	NOUN
m-392	29	3	or	or	CCONJ
m-392	29	4	nested	nested	ADJ
m-392	29	5	data	data	NOUN
m-392	29	6	design	design	NOUN
m-392	29	7	is	be	AUX
m-392	29	8	an	an	DET
m-392	29	9	experimental	experimental	ADJ
m-392	29	10	design	design	NOUN
m-392	29	11	technique	technique	NOUN
m-392	29	12	in	in	ADP
m-392	29	13	which	which	PRON
m-392	29	14	data	datum	NOUN
m-392	29	15	has	have	VERB
m-392	29	16	an	an	DET
m-392	29	17	implicit	implicit	ADJ
m-392	29	18	hierarchy	hierarchy	NOUN
m-392	29	19	.	.	PUNCT
m-392	30	1	the	the	DET
m-392	30	2	clusters	cluster	NOUN
m-392	30	3	may	may	AUX
m-392	30	4	be	be	AUX
m-392	30	5	balanced	balance	VERB
m-392	30	6	or	or	CCONJ
m-392	30	7	unbalanced	unbalanced	ADJ
m-392	30	8	,	,	PUNCT
m-392	30	9	i.e.	i.e.	X
m-392	30	10	,	,	PUNCT
m-392	30	11	the	the	DET
m-392	30	12	number	number	NOUN
m-392	30	13	of	of	ADP
m-392	30	14	observations	observation	NOUN
m-392	30	15	in	in	ADP
m-392	30	16	a	a	DET
m-392	30	17	cluster	cluster	NOUN
m-392	30	18	(	(	PUNCT
m-392	30	19	the	the	DET
m-392	30	20	size	size	NOUN
m-392	30	21	of	of	ADP
m-392	30	22	the	the	DET
m-392	30	23	cluster	cluster	NOUN
m-392	30	24	)	)	PUNCT
m-392	30	25	is	be	AUX
m-392	30	26	equal	equal	ADJ
m-392	30	27	or	or	CCONJ
m-392	30	28	unequal	unequal	ADJ
m-392	30	29	.	.	PUNCT
m-392	31	1	the	the	DET
m-392	31	2	unbalanced	unbalanced	ADJ
m-392	31	3	clustered	clustered	ADJ
m-392	31	4	data	datum	NOUN
m-392	31	5	may	may	AUX
m-392	31	6	bring	bring	VERB
m-392	31	7	up	up	ADP
m-392	31	8	the	the	DET
m-392	31	9	problem	problem	NOUN
m-392	31	10	of	of	ADP
m-392	31	11	heterogeneous	heterogeneous	ADJ
m-392	31	12	models	model	NOUN
m-392	31	13	which	which	PRON
m-392	31	14	require	require	VERB
m-392	31	15	different	different	ADJ
m-392	31	16	variance	variance	NOUN
m-392	31	17	components	component	NOUN
m-392	31	18	,	,	PUNCT
m-392	31	19	as	as	SCONJ
m-392	31	20	had	have	AUX
m-392	31	21	been	be	AUX
m-392	31	22	addressed	address	VERB
m-392	31	23	in	in	ADP
m-392	31	24	previous	previous	ADJ
m-392	31	25	studies	study	NOUN
m-392	31	26	for	for	ADP
m-392	31	27	continuous	continuous	ADJ
m-392	31	28	response	response	NOUN
m-392	31	29	(	(	PUNCT
m-392	31	30	el	el	PROPN
m-392	31	31	-	-	PUNCT
m-392	31	32	saeiti	saeiti	PROPN
m-392	31	33	,	,	PUNCT
m-392	31	34	2015	2015	NUM
m-392	31	35	)	)	PUNCT
m-392	31	36	.	.	PUNCT
m-392	32	1	in	in	ADP
m-392	32	2	the	the	DET
m-392	32	3	case	case	NOUN
m-392	32	4	of	of	ADP
m-392	32	5	unbalanced	unbalanced	ADJ
m-392	32	6	clustered	clustered	ADJ
m-392	32	7	data	datum	NOUN
m-392	32	8	with	with	ADP
m-392	32	9	continuous	continuous	ADJ
m-392	32	10	outcomes	outcome	NOUN
m-392	32	11	in	in	ADP
m-392	32	12	the	the	DET
m-392	32	13	linear	linear	PROPN
m-392	32	14	model	model	NOUN
m-392	32	15	,	,	PUNCT
m-392	32	16	el	el	PROPN
m-392	32	17	-	-	PROPN
m-392	32	18	saeiti	saeiti	PROPN
m-392	32	19	(	(	PUNCT
m-392	32	20	2015	2015	NUM
m-392	32	21	)	)	PUNCT
m-392	32	22	found	find	VERB
m-392	32	23	that	that	SCONJ
m-392	32	24	,	,	PUNCT
m-392	32	25	there	there	PRON
m-392	32	26	was	be	VERB
m-392	32	27	a	a	DET
m-392	32	28	ijo	ijo	PROPN
m-392	32	29	international	international	ADJ
m-392	32	30	journal	journal	PROPN
m-392	32	31	of	of	ADP
m-392	32	32	mathematics	mathematics	PROPN
m-392	32	33	volume	volume	PROPN
m-392	32	34	3|	3|	NUM
m-392	32	35	issue	issue	NOUN
m-392	33	1	12|	12|	NUM
m-392	33	2	december	december	PROPN
m-392	33	3	|	|	NOUN
m-392	33	4	2020	2020	NUM
m-392	33	5	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	33	6	19	19	NUM
m-392	33	7	different	different	ADJ
m-392	33	8	dispersions	dispersion	NOUN
m-392	33	9	for	for	ADP
m-392	33	10	different	different	ADJ
m-392	33	11	clusters	cluster	NOUN
m-392	33	12	sizes	size	NOUN
m-392	33	13	.	.	PUNCT
m-392	34	1	ac	ac	PROPN
m-392	34	2	-	-	PUNCT
m-392	34	3	counting	counting	NOUN
m-392	34	4	for	for	ADP
m-392	34	5	the	the	DET
m-392	34	6	different	different	ADJ
m-392	34	7	dispersions	dispersion	NOUN
m-392	34	8	led	lead	VERB
m-392	34	9	to	to	ADP
m-392	34	10	the	the	DET
m-392	34	11	minimization	minimization	NOUN
m-392	34	12	of	of	ADP
m-392	34	13	mean	mean	ADJ
m-392	34	14	square	square	ADJ
m-392	34	15	error	error	NOUN
m-392	34	16	,	,	PUNCT
m-392	34	17	which	which	PRON
m-392	34	18	was	be	AUX
m-392	34	19	shown	show	VERB
m-392	34	20	through	through	ADP
m-392	34	21	two	two	NUM
m-392	34	22	examples	example	NOUN
m-392	34	23	.	.	PUNCT
m-392	35	1	in	in	ADP
m-392	35	2	this	this	DET
m-392	35	3	study	study	NOUN
m-392	35	4	,	,	PUNCT
m-392	35	5	the	the	DET
m-392	35	6	researcher	researcher	NOUN
m-392	35	7	focused	focus	VERB
m-392	35	8	on	on	ADP
m-392	35	9	the	the	DET
m-392	35	10	counting	counting	NOUN
m-392	35	11	outcomes	outcome	NOUN
m-392	35	12	.	.	PUNCT
m-392	36	1	when	when	SCONJ
m-392	36	2	using	use	VERB
m-392	36	3	mixed	mixed	ADJ
m-392	36	4	effects	effect	NOUN
m-392	36	5	for	for	ADP
m-392	36	6	clustered	clustered	ADJ
m-392	36	7	data	datum	NOUN
m-392	36	8	with	with	ADP
m-392	36	9	counting	count	VERB
m-392	36	10	outcomes	outcome	NOUN
m-392	36	11	,	,	PUNCT
m-392	36	12	a	a	DET
m-392	36	13	preferred	preferred	ADJ
m-392	36	14	model	model	NOUN
m-392	36	15	is	be	AUX
m-392	36	16	hierarchical	hierarchical	ADJ
m-392	36	17	generalized	generalized	ADJ
m-392	36	18	linear	linear	ADJ
m-392	36	19	model	model	NOUN
m-392	36	20	(	(	PUNCT
m-392	36	21	hglm	hglm	NOUN
m-392	36	22	)	)	PUNCT
m-392	36	23	.	.	PUNCT
m-392	37	1	lee	lee	PROPN
m-392	37	2	and	and	CCONJ
m-392	37	3	ryan	ryan	PROPN
m-392	37	4	(	(	PUNCT
m-392	37	5	2017	2017	NUM
m-392	37	6	)	)	PUNCT
m-392	37	7	are	be	AUX
m-392	37	8	concerned	concern	VERB
m-392	37	9	with	with	ADP
m-392	37	10	a	a	DET
m-392	37	11	class	class	NOUN
m-392	37	12	of	of	ADP
m-392	37	13	generalized	generalized	ADJ
m-392	37	14	linear	linear	ADJ
m-392	37	15	mixed	mix	VERB
m-392	37	16	models	model	NOUN
m-392	37	17	for	for	ADP
m-392	37	18	clustered	clustered	ADJ
m-392	37	19	data	datum	NOUN
m-392	37	20	,	,	PUNCT
m-392	37	21	where	where	SCONJ
m-392	37	22	random	random	ADJ
m-392	37	23	effects	effect	NOUN
m-392	37	24	are	be	AUX
m-392	37	25	mapped	map	VERB
m-392	37	26	solely	solely	ADV
m-392	37	27	to	to	ADP
m-392	37	28	cluster	cluster	NOUN
m-392	37	29	structure	structure	NOUN
m-392	37	30	and	and	CCONJ
m-392	37	31	are	be	AUX
m-392	37	32	independent	independent	ADJ
m-392	37	33	between	between	ADP
m-392	37	34	groups	group	NOUN
m-392	37	35	;	;	PUNCT
m-392	37	36	they	they	PRON
m-392	37	37	derive	derive	VERB
m-392	37	38	the	the	DET
m-392	37	39	necessary	necessary	ADJ
m-392	37	40	and	and	CCONJ
m-392	37	41	sufficient	sufficient	ADJ
m-392	37	42	conditions	condition	NOUN
m-392	37	43	that	that	PRON
m-392	37	44	allow	allow	VERB
m-392	37	45	the	the	DET
m-392	37	46	marginal	marginal	ADJ
m-392	37	47	likelihood	likelihood	NOUN
m-392	37	48	of	of	ADP
m-392	37	49	such	such	DET
m-392	37	50	a	a	DET
m-392	37	51	class	class	NOUN
m-392	37	52	of	of	ADP
m-392	37	53	models	model	NOUN
m-392	37	54	to	to	PART
m-392	37	55	be	be	AUX
m-392	37	56	expressed	express	VERB
m-392	37	57	in	in	ADP
m-392	37	58	closed	closed	ADJ
m-392	37	59	form	form	NOUN
m-392	37	60	.	.	PUNCT
m-392	38	1	illustrations	illustration	NOUN
m-392	38	2	are	be	AUX
m-392	38	3	provided	provide	VERB
m-392	38	4	using	use	VERB
m-392	38	5	normal	normal	ADJ
m-392	38	6	,	,	PUNCT
m-392	38	7	poisson	poisson	NOUN
m-392	38	8	,	,	PUNCT
m-392	38	9	binomial	binomial	ADJ
m-392	38	10	and	and	CCONJ
m-392	38	11	gamma	gamma	NOUN
m-392	38	12	distributions	distribution	NOUN
m-392	38	13	;	;	PUNCT
m-392	38	14	these	these	DET
m-392	38	15	models	model	NOUN
m-392	38	16	are	be	AUX
m-392	38	17	unified	unify	VERB
m-392	38	18	under	under	ADP
m-392	38	19	a	a	DET
m-392	38	20	single	single	ADJ
m-392	38	21	umbrella	umbrella	NOUN
m-392	38	22	of	of	ADP
m-392	38	23	generalized	generalized	ADJ
m-392	38	24	conjugate	conjugate	ADJ
m-392	38	25	linear	linear	ADJ
m-392	38	26	mixed	mixed	ADJ
m-392	38	27	models	model	NOUN
m-392	38	28	,	,	PUNCT
m-392	38	29	where	where	SCONJ
m-392	38	30	"	"	PUNCT
m-392	38	31	conjugate	conjugate	ADJ
m-392	38	32	"	"	PUNCT
m-392	38	33	refers	refer	VERB
m-392	38	34	to	to	ADP
m-392	38	35	the	the	DET
m-392	38	36	fact	fact	NOUN
m-392	38	37	that	that	SCONJ
m-392	38	38	marginal	marginal	ADJ
m-392	38	39	likelihood	likelihood	NOUN
m-392	38	40	can	can	AUX
m-392	38	41	be	be	AUX
m-392	38	42	expressed	express	VERB
m-392	38	43	in	in	ADP
m-392	38	44	closed	closed	ADJ
m-392	38	45	form	form	NOUN
m-392	38	46	,	,	PUNCT
m-392	38	47	rather	rather	ADV
m-392	38	48	than	than	ADP
m-392	38	49	implying	imply	VERB
m-392	38	50	inference	inference	NOUN
m-392	38	51	through	through	ADP
m-392	38	52	the	the	DET
m-392	38	53	bayesian	bayesian	NOUN
m-392	38	54	paradigm	paradigm	NOUN
m-392	38	55	.	.	PUNCT
m-392	39	1	using	use	VERB
m-392	39	2	an	an	DET
m-392	39	3	explicit	explicit	ADJ
m-392	39	4	marginal	marginal	ADJ
m-392	39	5	likelihood	likelihood	NOUN
m-392	39	6	means	mean	VERB
m-392	39	7	that	that	SCONJ
m-392	39	8	these	these	DET
m-392	39	9	models	model	NOUN
m-392	39	10	are	be	AUX
m-392	39	11	more	more	ADV
m-392	39	12	computationally	computationally	ADV
m-392	39	13	efficient	efficient	ADJ
m-392	39	14	,	,	PUNCT
m-392	39	15	which	which	PRON
m-392	39	16	can	can	AUX
m-392	39	17	be	be	AUX
m-392	39	18	important	important	ADJ
m-392	39	19	in	in	ADP
m-392	39	20	large	large	ADJ
m-392	39	21	data	datum	NOUN
m-392	39	22	environments	environment	NOUN
m-392	39	23	,	,	PUNCT
m-392	39	24	with	with	ADP
m-392	39	25	the	the	DET
m-392	39	26	exception	exception	NOUN
m-392	39	27	of	of	ADP
m-392	39	28	binomial	binomial	ADJ
m-392	39	29	distribution	distribution	NOUN
m-392	39	30	,	,	PUNCT
m-392	39	31	so	so	SCONJ
m-392	39	32	that	that	SCONJ
m-392	39	33	these	these	DET
m-392	39	34	models	model	NOUN
m-392	39	35	are	be	AUX
m-392	39	36	able	able	ADJ
m-392	39	37	to	to	PART
m-392	39	38	achieve	achieve	VERB
m-392	39	39	conjugation	conjugation	NOUN
m-392	39	40	at	at	ADP
m-392	39	41	the	the	DET
m-392	39	42	same	same	ADJ
m-392	39	43	time	time	NOUN
m-392	39	44	and	and	CCONJ
m-392	39	45	thus	thus	ADV
m-392	39	46	be	be	AUX
m-392	39	47	able	able	ADJ
m-392	39	48	to	to	PART
m-392	39	49	accommodate	accommodate	VERB
m-392	39	50	both	both	DET
m-392	39	51	unit	unit	NOUN
m-392	39	52	and	and	CCONJ
m-392	39	53	group	group	NOUN
m-392	39	54	level	level	NOUN
m-392	39	55	covariates	covariate	VERB
m-392	39	56	.	.	PUNCT
m-392	40	1	theoretical	theoretical	ADJ
m-392	40	2	background	background	NOUN
m-392	40	3	poisson	poisson	PROPN
m-392	40	4	-	-	PUNCT
m-392	40	5	gamma	gamma	PROPN
m-392	40	6	hglm	hglm	NOUN
m-392	40	7	are	be	AUX
m-392	40	8	members	member	NOUN
m-392	40	9	of	of	ADP
m-392	40	10	the	the	DET
m-392	40	11	hierarchical	hierarchical	ADJ
m-392	40	12	generalized	generalized	ADJ
m-392	40	13	linear	linear	ADJ
m-392	40	14	model	model	NOUN
m-392	40	15	family	family	NOUN
m-392	40	16	(	(	PUNCT
m-392	40	17	lee	lee	PROPN
m-392	40	18	and	and	CCONJ
m-392	40	19	nelder	nelder	ADJ
m-392	40	20	,	,	PUNCT
m-392	40	21	1996	1996	NUM
m-392	40	22	)	)	PUNCT
m-392	40	23	,	,	PUNCT
m-392	40	24	an	an	DET
m-392	40	25	extension	extension	NOUN
m-392	40	26	of	of	ADP
m-392	40	27	the	the	DET
m-392	40	28	generalized	generalize	VERB
m-392	40	29	linear	linear	PROPN
m-392	40	30	model	model	NOUN
m-392	40	31	family	family	NOUN
m-392	40	32	and	and	CCONJ
m-392	40	33	the	the	DET
m-392	40	34	generalized	generalize	VERB
m-392	40	35	linear	linear	ADJ
m-392	40	36	mixed	mixed	ADJ
m-392	40	37	model	model	NOUN
m-392	40	38	group	group	NOUN
m-392	40	39	.	.	PUNCT
m-392	41	1	for	for	ADP
m-392	41	2	training	training	NOUN
m-392	41	3	,	,	PUNCT
m-392	41	4	poisson	poisson	PROPN
m-392	41	5	-	-	PUNCT
m-392	41	6	gamma	gamma	PROPN
m-392	41	7	hglm	hglm	NOUN
m-392	41	8	is	be	AUX
m-392	41	9	used	use	VERB
m-392	41	10	to	to	PART
m-392	41	11	characterize	characterize	VERB
m-392	41	12	historical	historical	ADJ
m-392	41	13	count	count	NOUN
m-392	41	14	data	datum	NOUN
m-392	41	15	as	as	ADP
m-392	41	16	non	non	ADJ
m-392	41	17	-	-	ADJ
m-392	41	18	life	life	ADJ
m-392	41	19	insurance	insurance	NOUN
m-392	41	20	compensation	compensation	NOUN
m-392	41	21	numbers	number	NOUN
m-392	41	22	,	,	PUNCT
m-392	41	23	among	among	ADP
m-392	41	24	others	other	NOUN
m-392	41	25	.	.	PUNCT
m-392	42	1	it	it	PRON
m-392	42	2	should	should	AUX
m-392	42	3	be	be	AUX
m-392	42	4	remembered	remember	VERB
m-392	42	5	that	that	SCONJ
m-392	42	6	the	the	DET
m-392	42	7	poisson	poisson	PROPN
m-392	42	8	gamma	gamma	PROPN
m-392	42	9	hglm	hglm	PROPN
m-392	42	10	considered	consider	VERB
m-392	42	11	at	at	ADP
m-392	42	12	one	one	NUM
m-392	42	13	time	time	NOUN
m-392	42	14	follows	follow	VERB
m-392	42	15	a	a	DET
m-392	42	16	negative	negative	ADJ
m-392	42	17	binomial	binomial	ADJ
m-392	42	18	regression	regression	NOUN
m-392	42	19	model	model	NOUN
m-392	42	20	(	(	PUNCT
m-392	42	21	gning	gning	NOUN
m-392	42	22	,	,	PUNCT
m-392	42	23	2013	2013	NUM
m-392	42	24	)	)	PUNCT
m-392	42	25	.	.	PUNCT
m-392	43	1	modeling	model	VERB
m-392	43	2	poisson	poisson	PROPN
m-392	43	3	data	datum	NOUN
m-392	43	4	yi	yi	PROPN
m-392	43	5	∼	∼	NOUN
m-392	43	6	poisson(λi	poisson(λi	PROPN
m-392	43	7	)	)	PUNCT
m-392	43	8	then	then	ADV
m-392	43	9	;	;	PUNCT
m-392	43	10	e(yi	e(yi	X
m-392	43	11	)	)	PUNCT
m-392	43	12	=	=	SYM
m-392	44	1	λi	λi	NOUN
m-392	44	2	and	and	CCONJ
m-392	44	3	var	var	NOUN
m-392	44	4	(	(	PUNCT
m-392	44	5	yi	yi	NOUN
m-392	44	6	)	)	PUNCT
m-392	44	7	=	=	VERB
m-392	45	1	λi	λi	X
m-392	45	2	.	.	PUNCT
m-392	46	1	the	the	DET
m-392	46	2	link	link	NOUN
m-392	46	3	function	function	NOUN
m-392	46	4	must	must	AUX
m-392	46	5	map	map	VERB
m-392	46	6	from	from	ADP
m-392	46	7	(	(	PUNCT
m-392	46	8	0	0	NUM
m-392	46	9	,	,	PUNCT
m-392	46	10	∞	∞	PROPN
m-392	46	11	)	)	PUNCT
m-392	46	12	to	to	ADP
m-392	46	13	(	(	PUNCT
m-392	46	14	∞	∞	PROPN
m-392	46	15	,	,	PUNCT
m-392	46	16	∞	∞	PROPN
m-392	46	17	)	)	PUNCT
m-392	46	18	.	.	PUNCT
m-392	47	1	a	a	DET
m-392	47	2	natural	natural	ADJ
m-392	47	3	choice	choice	NOUN
m-392	47	4	is	be	AUX
m-392	47	5	g(µi	g(µi	X
m-392	47	6	)	)	PUNCT
m-392	47	7	=	=	SYM
m-392	47	8	log(µi	log(µi	PROPN
m-392	47	9	)	)	PUNCT
m-392	47	10	.	.	PUNCT
m-392	48	1	for	for	ADP
m-392	48	2	dependent	dependent	ADJ
m-392	48	3	count	count	NOUN
m-392	48	4	data	datum	NOUN
m-392	48	5	(	(	PUNCT
m-392	48	6	rönnegård	rönnegård	PROPN
m-392	48	7	and	and	CCONJ
m-392	48	8	shen	shen	PROPN
m-392	48	9	,	,	PUNCT
m-392	48	10	2010	2010	NUM
m-392	48	11	)	)	PUNCT
m-392	48	12	it	it	PRON
m-392	48	13	has	have	AUX
m-392	48	14	been	be	AUX
m-392	48	15	stated	state	VERB
m-392	48	16	that	that	SCONJ
m-392	48	17	it	it	PRON
m-392	48	18	is	be	AUX
m-392	48	19	common	common	ADJ
m-392	48	20	to	to	PART
m-392	48	21	model	model	VERB
m-392	48	22	a	a	DET
m-392	48	23	distributed	distribute	VERB
m-392	48	24	poisson	poisson	NOUN
m-392	48	25	response	response	NOUN
m-392	48	26	with	with	ADP
m-392	48	27	a	a	DET
m-392	48	28	random	random	ADJ
m-392	48	29	gamma	gamma	NOUN
m-392	48	30	effect	effect	NOUN
m-392	48	31	;	;	PUNCT
m-392	48	32	if	if	SCONJ
m-392	48	33	no	no	DET
m-392	48	34	overdispersion	overdispersion	NOUN
m-392	48	35	is	be	AUX
m-392	48	36	assumed	assume	VERB
m-392	48	37	to	to	PART
m-392	48	38	be	be	AUX
m-392	48	39	conditional	conditional	ADJ
m-392	48	40	on	on	ADP
m-392	48	41	u	u	NOUN
m-392	48	42	and	and	CCONJ
m-392	48	43	thus	thus	ADV
m-392	48	44	have	have	VERB
m-392	48	45	a	a	DET
m-392	48	46	fixed	fix	VERB
m-392	48	47	dispersion	dispersion	NOUN
m-392	48	48	term	term	NOUN
m-392	48	49	;	;	PUNCT
m-392	48	50	this	this	DET
m-392	48	51	model	model	NOUN
m-392	48	52	may	may	AUX
m-392	48	53	be	be	AUX
m-392	48	54	specified	specify	VERB
m-392	48	55	as	as	ADP
m-392	48	56	.	.	PROPN
m-392	48	57	�	�	PROPN
m-392	48	58	(	(	PUNCT
m-392	48	59	�	�	PROPN
m-392	48	60	�	�	PROPN
m-392	48	61	|	|	NOUN
m-392	48	62	�	�	PROPN
m-392	48	63	,	,	PUNCT
m-392	48	64	�	�	PROPN
m-392	48	65	)	)	PUNCT
m-392	48	66	=	=	SYM
m-392	48	67	�	�	PROPN
m-392	48	68	�	�	PROPN
m-392	48	69	�	�	PROPN
m-392	48	70	(	(	PUNCT
m-392	48	71	�	�	PROPN
m-392	48	72	�	�	PROPN
m-392	48	73	�	�	PROPN
m-392	48	74	+	+	SYM
m-392	48	75	�	�	PROPN
m-392	48	76	�	�	PROPN
m-392	48	77	�	�	PROPN
m-392	48	78	)	)	PUNCT
m-392	48	79	..	..	PUNCT
m-392	48	80	(	(	PUNCT
m-392	48	81	3	3	X
m-392	48	82	)	)	PUNCT
m-392	48	83	ijo	ijo	PROPN
m-392	48	84	international	international	PROPN
m-392	48	85	journal	journal	PROPN
m-392	48	86	of	of	ADP
m-392	48	87	mathematics	mathematics	PROPN
m-392	48	88	volume	volume	PROPN
m-392	48	89	3|	3|	NUM
m-392	48	90	issue	issue	NOUN
m-392	48	91	12|	12|	NUM
m-392	48	92	december	december	PROPN
m-392	48	93	|	|	NOUN
m-392	48	94	2020	2020	NUM
m-392	48	95	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	48	96	20	20	NUM
m-392	48	97	lee	lee	PROPN
m-392	48	98	and	and	CCONJ
m-392	48	99	nelder	nelder	NOUN
m-392	48	100	(	(	PUNCT
m-392	48	101	1996	1996	NUM
m-392	48	102	)	)	PUNCT
m-392	48	103	described	describe	VERB
m-392	48	104	the	the	DET
m-392	48	105	generalized	generalized	ADJ
m-392	48	106	linear	linear	ADJ
m-392	48	107	model	model	NOUN
m-392	48	108	for	for	ADP
m-392	48	109	poisson	poisson	PROPN
m-392	48	110	-	-	PUNCT
m-392	48	111	gamma	gamma	NOUN
m-392	48	112	hierarchical	hierarchical	ADJ
m-392	48	113	and	and	CCONJ
m-392	48	114	the	the	DET
m-392	48	115	generalized	generalize	VERB
m-392	48	116	linear	linear	ADJ
m-392	48	117	mixed	mixed	ADJ
m-392	48	118	form	form	NOUN
m-392	48	119	structure	structure	NOUN
m-392	48	120	of	of	ADP
m-392	48	121	poission	poission	NOUN
m-392	48	122	.	.	PUNCT
m-392	49	1	the	the	DET
m-392	49	2	three	three	NUM
m-392	49	3	pices	pice	NOUN
m-392	49	4	of	of	ADP
m-392	49	5	hglm	hglm	NOUN
m-392	49	6	for	for	ADP
m-392	49	7	poissongamma	poissongamma	PROPN
m-392	49	8	is	be	AUX
m-392	49	9	:	:	PUNCT
m-392	49	10	1	1	X
m-392	49	11	.	.	X
m-392	49	12	yij|	yij|	NOUN
m-392	49	13	uj	uj	PROPN
m-392	49	14	~	~	PUNCT
m-392	49	15	poi	poi	X
m-392	49	16	(	(	PUNCT
m-392	49	17	λi	λi	PROPN
m-392	49	18	,	,	PUNCT
m-392	49	19	ϕ	ϕ	NOUN
m-392	49	20	λi	λi	NOUN
m-392	49	21	)	)	PUNCT
m-392	49	22	)	)	PUNCT
m-392	49	23	,	,	PUNCT
m-392	49	24	ui	ui	NOUN
m-392	49	25	~	~	PUNCT
m-392	49	26	gamm(α	gamm(α	PROPN
m-392	49	27	,	,	PUNCT
m-392	49	28	γi	γi	NOUN
m-392	49	29	)	)	PUNCT
m-392	49	30	,	,	PUNCT
m-392	49	31	2	2	X
m-392	49	32	.	.	PUNCT
m-392	49	33	η	η	PROPN
m-392	49	34	=	=	PROPN
m-392	49	35	xβ	xβ	PROPN
m-392	50	1	+	+	CCONJ
m-392	50	2	zu	zu	X
m-392	50	3	,	,	PUNCT
m-392	50	4	3	3	X
m-392	50	5	.	.	PUNCT
m-392	50	6	η	η	PROPN
m-392	50	7	=	=	SYM
m-392	50	8	ln(λi	ln(λi	X
m-392	50	9	)	)	PUNCT
m-392	50	10	more	more	ADJ
m-392	50	11	details	detail	NOUN
m-392	50	12	on	on	ADP
m-392	50	13	poisson	poisson	PROPN
m-392	50	14	-	-	PUNCT
m-392	50	15	gamma	gamma	PROPN
m-392	50	16	model	model	NOUN
m-392	50	17	see	see	VERB
m-392	50	18	(	(	PUNCT
m-392	50	19	lee	lee	PROPN
m-392	50	20	and	and	CCONJ
m-392	50	21	nelder	nelder	NOUN
m-392	50	22	1996	1996	NUM
m-392	50	23	,	,	PUNCT
m-392	50	24	2001	2001	NUM
m-392	50	25	)	)	PUNCT
m-392	50	26	.	.	PUNCT
m-392	51	1	however	however	ADV
m-392	51	2	,	,	PUNCT
m-392	51	3	in	in	ADP
m-392	51	4	the	the	DET
m-392	51	5	clustered	clustered	ADJ
m-392	51	6	count	count	NOUN
m-392	51	7	response	response	NOUN
m-392	51	8	because	because	SCONJ
m-392	51	9	the	the	DET
m-392	51	10	assumption	assumption	NOUN
m-392	51	11	of	of	ADP
m-392	51	12	independence	independence	NOUN
m-392	51	13	between	between	ADP
m-392	51	14	cluster	cluster	NOUN
m-392	51	15	observations	observation	NOUN
m-392	51	16	is	be	AUX
m-392	51	17	likely	likely	ADJ
m-392	51	18	to	to	PART
m-392	51	19	be	be	AUX
m-392	51	20	violated	violate	VERB
m-392	51	21	,	,	PUNCT
m-392	51	22	a	a	DET
m-392	51	23	mixed	mixed	ADJ
m-392	51	24	effects	effect	NOUN
m-392	51	25	clustered	clustered	ADJ
m-392	51	26	counting	counting	NOUN
m-392	51	27	data	datum	NOUN
m-392	51	28	model	model	NOUN
m-392	51	29	is	be	AUX
m-392	51	30	a	a	DET
m-392	51	31	useful	useful	ADJ
m-392	51	32	strategy	strategy	NOUN
m-392	51	33	to	to	PART
m-392	51	34	account	account	VERB
m-392	51	35	for	for	ADP
m-392	51	36	intracluster	intracluster	ADJ
m-392	51	37	correlations	correlation	NOUN
m-392	51	38	in	in	ADP
m-392	51	39	statistical	statistical	ADJ
m-392	51	40	inference	inference	NOUN
m-392	51	41	,	,	PUNCT
m-392	51	42	see	see	VERB
m-392	51	43	hedeker	hedeker	NOUN
m-392	51	44	and	and	CCONJ
m-392	51	45	gibbons	gibbon	NOUN
m-392	51	46	(	(	PUNCT
m-392	51	47	1994	1994	NUM
m-392	51	48	)	)	PUNCT
m-392	51	49	.	.	PUNCT
m-392	52	1	the	the	DET
m-392	52	2	purpose	purpose	NOUN
m-392	52	3	of	of	ADP
m-392	52	4	this	this	DET
m-392	52	5	paper	paper	NOUN
m-392	52	6	is	be	AUX
m-392	52	7	to	to	PART
m-392	52	8	compare	compare	VERB
m-392	52	9	the	the	DET
m-392	52	10	performance	performance	NOUN
m-392	52	11	of	of	ADP
m-392	52	12	the	the	DET
m-392	52	13	mixed	mixed	ADJ
m-392	52	14	effects	effect	NOUN
m-392	52	15	clustered	cluster	VERB
m-392	52	16	data	datum	NOUN
m-392	52	17	count	count	NOUN
m-392	52	18	model	model	NOUN
m-392	52	19	with	with	ADP
m-392	52	20	equal	equal	ADJ
m-392	52	21	and	and	CCONJ
m-392	52	22	unequal	unequal	ADJ
m-392	52	23	cluster	cluster	NOUN
m-392	52	24	size	size	NOUN
m-392	52	25	.	.	PUNCT
m-392	53	1	here	here	ADV
m-392	53	2	,	,	PUNCT
m-392	53	3	the	the	DET
m-392	53	4	author	author	NOUN
m-392	53	5	discusses	discuss	VERB
m-392	53	6	the	the	DET
m-392	53	7	probability	probability	NOUN
m-392	53	8	of	of	ADP
m-392	53	9	type	type	NOUN
m-392	53	10	i	i	PRON
m-392	53	11	error	error	NOUN
m-392	53	12	rate	rate	NOUN
m-392	53	13	,	,	PUNCT
m-392	53	14	the	the	DET
m-392	53	15	statistical	statistical	ADJ
m-392	53	16	power	power	NOUN
m-392	53	17	of	of	ADP
m-392	53	18	the	the	DET
m-392	53	19	experiment	experiment	NOUN
m-392	53	20	,	,	PUNCT
m-392	53	21	and	and	CCONJ
m-392	53	22	the	the	DET
m-392	53	23	standard	standard	ADJ
m-392	53	24	error	error	NOUN
m-392	53	25	(	(	PUNCT
m-392	53	26	s.e	s.e	PROPN
m-392	53	27	)	)	PUNCT
m-392	53	28	by	by	ADP
m-392	53	29	computer	computer	NOUN
m-392	53	30	simulation	simulation	NOUN
m-392	53	31	study	study	PROPN
m-392	53	32	.	.	PUNCT
m-392	54	1	simulation	simulation	NOUN
m-392	54	2	study	study	VERB
m-392	54	3	the	the	DET
m-392	54	4	simplest	simple	ADJ
m-392	54	5	definition	definition	NOUN
m-392	54	6	of	of	ADP
m-392	54	7	simulation	simulation	NOUN
m-392	54	8	in	in	ADP
m-392	54	9	science	science	NOUN
m-392	54	10	is	be	AUX
m-392	54	11	that	that	SCONJ
m-392	54	12	it	it	PRON
m-392	54	13	is	be	AUX
m-392	54	14	a	a	DET
m-392	54	15	numerical	numerical	ADJ
m-392	54	16	method	method	NOUN
m-392	54	17	of	of	ADP
m-392	54	18	running	run	VERB
m-392	54	19	trails	trail	NOUN
m-392	54	20	or	or	CCONJ
m-392	54	21	tests	test	NOUN
m-392	54	22	using	use	VERB
m-392	54	23	computer	computer	NOUN
m-392	54	24	algorithms	algorithm	NOUN
m-392	54	25	instead	instead	ADV
m-392	54	26	of	of	ADP
m-392	54	27	conducting	conduct	VERB
m-392	54	28	a	a	DET
m-392	54	29	real	real	ADJ
m-392	54	30	experiment	experiment	NOUN
m-392	54	31	.	.	PUNCT
m-392	55	1	simulation	simulation	NOUN
m-392	55	2	is	be	AUX
m-392	55	3	an	an	DET
m-392	55	4	approach	approach	NOUN
m-392	55	5	to	to	ADP
m-392	55	6	modeling	model	VERB
m-392	55	7	random	random	ADJ
m-392	55	8	events	event	NOUN
m-392	55	9	in	in	ADP
m-392	55	10	such	such	DET
m-392	55	11	a	a	DET
m-392	55	12	way	way	NOUN
m-392	55	13	that	that	PRON
m-392	55	14	simulated	simulate	VERB
m-392	55	15	outcomes	outcome	NOUN
m-392	55	16	closely	closely	ADV
m-392	55	17	match	match	VERB
m-392	55	18	real	real	ADJ
m-392	55	19	-	-	PUNCT
m-392	55	20	world	world	NOUN
m-392	55	21	outcomes	outcome	NOUN
m-392	55	22	,	,	PUNCT
m-392	55	23	and	and	CCONJ
m-392	55	24	by	by	ADP
m-392	55	25	studying	study	VERB
m-392	55	26	simulated	simulated	ADJ
m-392	55	27	outcomes	outcome	NOUN
m-392	55	28	,	,	PUNCT
m-392	55	29	researchers	researcher	NOUN
m-392	55	30	gain	gain	VERB
m-392	55	31	knowledge	knowledge	NOUN
m-392	55	32	of	of	ADP
m-392	55	33	the	the	DET
m-392	55	34	real	real	ADJ
m-392	55	35	world	world	NOUN
m-392	55	36	.	.	PUNCT
m-392	56	1	in	in	ADP
m-392	56	2	other	other	ADJ
m-392	56	3	words	word	NOUN
m-392	56	4	,	,	PUNCT
m-392	56	5	the	the	DET
m-392	56	6	simulation	simulation	NOUN
m-392	56	7	of	of	ADP
m-392	56	8	a	a	DET
m-392	56	9	system	system	NOUN
m-392	56	10	is	be	AUX
m-392	56	11	the	the	DET
m-392	56	12	operation	operation	NOUN
m-392	56	13	of	of	ADP
m-392	56	14	a	a	DET
m-392	56	15	process	process	NOUN
m-392	56	16	model	model	NOUN
m-392	56	17	(	(	PUNCT
m-392	56	18	maria	maria	PROPN
m-392	56	19	,	,	PUNCT
m-392	56	20	1997	1997	NUM
m-392	56	21	)	)	PUNCT
m-392	56	22	.	.	PUNCT
m-392	57	1	the	the	DET
m-392	57	2	design	design	NOUN
m-392	57	3	can	can	AUX
m-392	57	4	be	be	AUX
m-392	57	5	re	re	VERB
m-392	57	6	-	-	VERB
m-392	57	7	fitted	fit	VERB
m-392	57	8	and	and	CCONJ
m-392	57	9	tested	test	VERB
m-392	57	10	at	at	ADP
m-392	57	11	a	a	DET
m-392	57	12	lower	low	ADJ
m-392	57	13	cost	cost	NOUN
m-392	57	14	,	,	PUNCT
m-392	57	15	so	so	CCONJ
m-392	57	16	simulation	simulation	NOUN
m-392	57	17	will	will	AUX
m-392	57	18	be	be	AUX
m-392	57	19	more	more	ADV
m-392	57	20	realistic	realistic	ADJ
m-392	57	21	.	.	PUNCT
m-392	58	1	the	the	DET
m-392	58	2	function	function	NOUN
m-392	58	3	of	of	ADP
m-392	58	4	the	the	DET
m-392	58	5	prototype	prototype	NOUN
m-392	58	6	can	can	AUX
m-392	58	7	be	be	AUX
m-392	58	8	investigated	investigate	VERB
m-392	58	9	and	and	CCONJ
m-392	58	10	thus	thus	ADV
m-392	58	11	inferences	inference	NOUN
m-392	58	12	can	can	AUX
m-392	58	13	be	be	AUX
m-392	58	14	made	make	VERB
m-392	58	15	about	about	ADP
m-392	58	16	the	the	DET
m-392	58	17	behavior	behavior	NOUN
m-392	58	18	of	of	ADP
m-392	58	19	the	the	DET
m-392	58	20	real	real	ADJ
m-392	58	21	system	system	NOUN
m-392	58	22	.	.	PUNCT
m-392	59	1	simulation	simulation	NOUN
m-392	59	2	can	can	AUX
m-392	59	3	also	also	ADV
m-392	59	4	be	be	AUX
m-392	59	5	viewed	view	VERB
m-392	59	6	as	as	ADP
m-392	59	7	a	a	DET
m-392	59	8	method	method	NOUN
m-392	59	9	to	to	PART
m-392	59	10	test	test	VERB
m-392	59	11	the	the	DET
m-392	59	12	quality	quality	NOUN
m-392	59	13	of	of	ADP
m-392	59	14	the	the	DET
m-392	59	15	current	current	ADJ
m-392	59	16	or	or	CCONJ
m-392	59	17	proposed	propose	VERB
m-392	59	18	process	process	NOUN
m-392	59	19	.	.	PUNCT
m-392	60	1	in	in	ADP
m-392	60	2	numerical	numerical	ADJ
m-392	60	3	applications	application	NOUN
m-392	60	4	,	,	PUNCT
m-392	60	5	the	the	DET
m-392	60	6	word	word	NOUN
m-392	60	7	simulation	simulation	NOUN
m-392	60	8	usually	usually	ADV
m-392	60	9	involves	involve	VERB
m-392	60	10	the	the	DET
m-392	60	11	random	random	ADJ
m-392	60	12	sampling	sampling	NOUN
m-392	60	13	process	process	NOUN
m-392	60	14	of	of	ADP
m-392	60	15	the	the	DET
m-392	60	16	probability	probability	NOUN
m-392	60	17	distributions	distribution	NOUN
m-392	60	18	.	.	PUNCT
m-392	61	1	due	due	ADP
m-392	61	2	to	to	ADP
m-392	61	3	its	its	PRON
m-392	61	4	wide	wide	ADJ
m-392	61	5	use	use	NOUN
m-392	61	6	,	,	PUNCT
m-392	61	7	this	this	PRON
m-392	61	8	is	be	AUX
m-392	61	9	an	an	DET
m-392	61	10	important	important	ADJ
m-392	61	11	part	part	NOUN
m-392	61	12	of	of	ADP
m-392	61	13	the	the	DET
m-392	61	14	statistical	statistical	ADJ
m-392	61	15	study	study	NOUN
m-392	61	16	.	.	PUNCT
m-392	62	1	this	this	DET
m-392	62	2	significance	significance	NOUN
m-392	62	3	occurs	occur	VERB
m-392	62	4	in	in	ADP
m-392	62	5	many	many	ADJ
m-392	62	6	situations	situation	NOUN
m-392	62	7	when	when	SCONJ
m-392	62	8	it	it	PRON
m-392	62	9	is	be	AUX
m-392	62	10	difficult	difficult	ADJ
m-392	62	11	to	to	PART
m-392	62	12	find	find	VERB
m-392	62	13	statistical	statistical	ADJ
m-392	62	14	diagnosis	diagnosis	NOUN
m-392	62	15	,	,	PUNCT
m-392	62	16	or	or	CCONJ
m-392	62	17	time	time	NOUN
m-392	62	18	consuming	consume	VERB
m-392	62	19	,	,	PUNCT
m-392	62	20	or	or	CCONJ
m-392	62	21	costly	costly	ADJ
m-392	62	22	to	to	PART
m-392	62	23	carry	carry	VERB
m-392	62	24	out	out	ADP
m-392	62	25	an	an	DET
m-392	62	26	analysis	analysis	NOUN
m-392	62	27	.	.	PUNCT
m-392	63	1	statistical	statistical	ADJ
m-392	63	2	simulation	simulation	NOUN
m-392	63	3	can	can	AUX
m-392	63	4	be	be	AUX
m-392	63	5	used	use	VERB
m-392	63	6	simply	simply	ADV
m-392	63	7	by	by	ADP
m-392	63	8	specifying	specify	VERB
m-392	63	9	a	a	DET
m-392	63	10	statistical	statistical	ADJ
m-392	63	11	software	software	NOUN
m-392	63	12	that	that	PRON
m-392	63	13	uses	use	VERB
m-392	63	14	random	random	ADJ
m-392	63	15	numbers	number	NOUN
m-392	63	16	to	to	PART
m-392	63	17	produce	produce	VERB
m-392	63	18	the	the	DET
m-392	63	19	values	value	NOUN
m-392	63	20	of	of	ADP
m-392	63	21	random	random	ADJ
m-392	63	22	variables	variable	NOUN
m-392	63	23	with	with	ADP
m-392	63	24	the	the	DET
m-392	63	25	desired	desire	VERB
m-392	63	26	probability	probability	NOUN
m-392	63	27	distributions	distribution	NOUN
m-392	63	28	(	(	PUNCT
m-392	63	29	uniform	uniform	ADJ
m-392	63	30	,	,	PUNCT
m-392	63	31	normal	normal	ADJ
m-392	63	32	binomial	binomial	NOUN
m-392	63	33	,	,	PUNCT
m-392	63	34	etc	etc	X
m-392	63	35	.	.	X
m-392	63	36	)	)	PUNCT
m-392	63	37	that	that	PRON
m-392	63	38	have	have	AUX
m-392	63	39	been	be	AUX
m-392	63	40	achieved	achieve	VERB
m-392	63	41	in	in	ADP
m-392	63	42	this	this	DET
m-392	63	43	research	research	NOUN
m-392	63	44	.	.	PUNCT
m-392	64	1	ijo	ijo	PROPN
m-392	64	2	international	international	PROPN
m-392	64	3	journal	journal	PROPN
m-392	64	4	of	of	ADP
m-392	64	5	mathematics	mathematics	PROPN
m-392	64	6	volume	volume	PROPN
m-392	64	7	3|	3|	NUM
m-392	64	8	issue	issue	NOUN
m-392	64	9	12|	12|	NUM
m-392	64	10	december	december	PROPN
m-392	64	11	|	|	NOUN
m-392	64	12	2020	2020	NUM
m-392	64	13	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	ADP
m-392	64	14	21	21	NUM
m-392	64	15	for	for	ADP
m-392	64	16	data	data	NOUN
m-392	64	17	generation	generation	NOUN
m-392	64	18	and	and	CCONJ
m-392	64	19	all	all	DET
m-392	64	20	simulation	simulation	NOUN
m-392	64	21	steps	step	NOUN
m-392	64	22	is	be	AUX
m-392	64	23	included	include	VERB
m-392	64	24	in	in	ADP
m-392	64	25	the	the	DET
m-392	64	26	appendix	appendix	ADJ
m-392	64	27	section	section	NOUN
m-392	64	28	'	'	PUNCT
m-392	64	29	end	end	NOUN
m-392	64	30	of	of	ADP
m-392	64	31	this	this	DET
m-392	64	32	paper	paper	NOUN
m-392	64	33	'	'	PUNCT
m-392	64	34	.	.	PUNCT
m-392	65	1	for	for	ADP
m-392	65	2	more	more	ADJ
m-392	65	3	explanation	explanation	NOUN
m-392	65	4	and	and	CCONJ
m-392	65	5	detail	detail	NOUN
m-392	65	6	on	on	ADP
m-392	65	7	related	relate	VERB
m-392	65	8	simulation	simulation	NOUN
m-392	65	9	studies	study	NOUN
m-392	65	10	with	with	ADP
m-392	65	11	different	different	ADJ
m-392	65	12	dependent	dependent	ADJ
m-392	65	13	variables	variable	NOUN
m-392	65	14	and	and	CCONJ
m-392	65	15	other	other	ADJ
m-392	65	16	variables	variable	NOUN
m-392	65	17	for	for	ADP
m-392	65	18	different	different	ADJ
m-392	65	19	purposes	purpose	NOUN
m-392	65	20	,	,	PUNCT
m-392	65	21	see	see	VERB
m-392	65	22	el	el	PROPN
m-392	65	23	-	-	PROPN
m-392	65	24	saeiti	saeiti	PROPN
m-392	65	25	(	(	PUNCT
m-392	65	26	2013	2013	NUM
m-392	65	27	,	,	PUNCT
m-392	65	28	2019	2019	NUM
m-392	65	29	)	)	PUNCT
m-392	65	30	.	.	PUNCT
m-392	66	1	for	for	ADP
m-392	66	2	h	h	NOUN
m-392	66	3	-	-	PUNCT
m-392	66	4	likelihood	likelihood	NOUN
m-392	66	5	`	`	PUNCT
m-392	66	6	poisson	poisson	PROPN
m-392	66	7	gamma	gamma	PROPN
m-392	66	8	hglm	hglm	PROPN
m-392	66	9	'	'	PUNCT
m-392	66	10	,	,	PUNCT
m-392	66	11	it	it	PRON
m-392	66	12	was	be	AUX
m-392	66	13	used	use	VERB
m-392	66	14	hglm	hglm	NOUN
m-392	66	15	function	function	NOUN
m-392	66	16	in	in	ADP
m-392	66	17	hglm	hglm	NOUN
m-392	66	18	package	package	NOUN
m-392	66	19	for	for	ADP
m-392	66	20	traditional	traditional	ADJ
m-392	66	21	poisson	poisson	NOUN
m-392	66	22	gamma	gamma	NOUN
m-392	66	23	in	in	ADP
m-392	66	24	r	r	NOUN
m-392	66	25	throw	throw	NOUN
m-392	66	26	the	the	DET
m-392	66	27	simulation	simulation	NOUN
m-392	66	28	steps	step	NOUN
m-392	66	29	.	.	PUNCT
m-392	67	1	using	use	VERB
m-392	67	2	hglm	hglm	NOUN
m-392	67	3	function	function	NOUN
m-392	67	4	to	to	PART
m-392	67	5	get	get	VERB
m-392	67	6	the	the	DET
m-392	67	7	estimation	estimation	NOUN
m-392	67	8	of	of	ADP
m-392	67	9	parameters	parameter	NOUN
m-392	67	10	�	�	PROPN
m-392	67	11	and	and	CCONJ
m-392	67	12	t	t	PROPN
m-392	67	13	-	-	PUNCT
m-392	67	14	statistic	statistic	NOUN
m-392	67	15	with	with	ADP
m-392	67	16	p	p	NOUN
m-392	67	17	value	value	NOUN
m-392	67	18	to	to	PART
m-392	67	19	calculate	calculate	VERB
m-392	67	20	through	through	ADP
m-392	67	21	simulation	simulation	NOUN
m-392	67	22	.	.	PUNCT
m-392	68	1	results	result	NOUN
m-392	68	2	and	and	CCONJ
m-392	68	3	discussion	discussion	VERB
m-392	68	4	the	the	DET
m-392	68	5	following	follow	VERB
m-392	68	6	tables	table	NOUN
m-392	68	7	and	and	CCONJ
m-392	68	8	diagrams	diagram	NOUN
m-392	68	9	will	will	AUX
m-392	68	10	demonstrate	demonstrate	VERB
m-392	68	11	the	the	DET
m-392	68	12	results	result	NOUN
m-392	68	13	obtained	obtain	VERB
m-392	68	14	from	from	ADP
m-392	68	15	the	the	DET
m-392	68	16	simulation	simulation	NOUN
m-392	68	17	and	and	CCONJ
m-392	68	18	display	display	VERB
m-392	68	19	the	the	DET
m-392	68	20	probability	probability	NOUN
m-392	68	21	of	of	ADP
m-392	68	22	the	the	DET
m-392	68	23	type	type	NOUN
m-392	68	24	-	-	PUNCT
m-392	68	25	i	i	NOUN
m-392	68	26	error	error	NOUN
m-392	68	27	rate	rate	NOUN
m-392	68	28	in	in	ADP
m-392	68	29	table	table	NOUN
m-392	68	30	(	(	PUNCT
m-392	68	31	1	1	NUM
m-392	68	32	)	)	PUNCT
m-392	68	33	,	,	PUNCT
m-392	68	34	the	the	DET
m-392	68	35	approximation	approximation	NOUN
m-392	68	36	value	value	NOUN
m-392	68	37	of	of	ADP
m-392	68	38	the	the	DET
m-392	68	39	“	"	PUNCT
m-392	68	40	β	β	X
m-392	68	41	“	"	PUNCT
m-392	68	42	parameters	parameter	NOUN
m-392	68	43	in	in	ADP
m-392	68	44	table	table	NOUN
m-392	68	45	(	(	PUNCT
m-392	68	46	2	2	NUM
m-392	68	47	)	)	PUNCT
m-392	68	48	,	,	PUNCT
m-392	68	49	the	the	DET
m-392	68	50	power	power	NOUN
m-392	68	51	in	in	ADP
m-392	68	52	table	table	NOUN
m-392	68	53	(	(	PUNCT
m-392	68	54	3	3	NUM
m-392	68	55	)	)	PUNCT
m-392	68	56	and	and	CCONJ
m-392	68	57	the	the	DET
m-392	68	58	standard	standard	ADJ
m-392	68	59	error	error	NOUN
m-392	68	60	in	in	ADP
m-392	68	61	table	table	NOUN
m-392	68	62	(	(	PUNCT
m-392	68	63	4	4	NUM
m-392	68	64	)	)	PUNCT
m-392	68	65	.	.	PUNCT
m-392	69	1	table	table	NOUN
m-392	69	2	(	(	PUNCT
m-392	69	3	1	1	X
m-392	69	4	)	)	PUNCT
m-392	69	5	display	display	VERB
m-392	69	6	the	the	DET
m-392	69	7	probability	probability	NOUN
m-392	69	8	of	of	ADP
m-392	69	9	type	type	NOUN
m-392	69	10	-	-	PUNCT
m-392	69	11	i	i	NOUN
m-392	69	12	error	error	NOUN
m-392	69	13	rate	rate	NOUN
m-392	69	14	were	be	AUX
m-392	69	15	computed	compute	VERB
m-392	69	16	as	as	ADP
m-392	69	17	the	the	DET
m-392	69	18	proportion	proportion	NOUN
m-392	69	19	of	of	ADP
m-392	69	20	p	p	NOUN
m-392	69	21	values	value	NOUN
m-392	69	22	less	less	ADJ
m-392	69	23	than	than	ADP
m-392	69	24	0.05	0.05	NUM
m-392	69	25	under	under	ADP
m-392	69	26	a	a	DET
m-392	69	27	null	null	ADJ
m-392	69	28	hypothesis	hypothesis	NOUN
m-392	69	29	�	�	PROPN
m-392	69	30	�	�	PROPN
m-392	69	31	:	:	PUNCT
m-392	69	32	�	�	PROPN
m-392	69	33	�	�	PROPN
m-392	69	34	=	=	SYM
m-392	69	35	0	0	NUM
m-392	69	36	of	of	ADP
m-392	69	37	no	no	DET
m-392	69	38	treatments	treatment	NOUN
m-392	69	39	effect	effect	NOUN
m-392	69	40	when	when	SCONJ
m-392	69	41	we	we	PRON
m-392	69	42	rejected	reject	VERB
m-392	69	43	incorrectly	incorrectly	ADV
m-392	69	44	.	.	PUNCT
m-392	70	1	table	table	NOUN
m-392	70	2	(	(	PUNCT
m-392	70	3	1	1	NUM
m-392	70	4	):	):	PUNCT
m-392	70	5	probability	probability	NOUN
m-392	70	6	of	of	ADP
m-392	70	7	type	type	NOUN
m-392	70	8	-	-	PUNCT
m-392	70	9	i	i	NOUN
m-392	70	10	error	error	NOUN
m-392	70	11	rate	rate	NOUN
m-392	70	12	cluster	cluster	NOUN
m-392	70	13	observations	observation	NOUN
m-392	70	14	unbalanced	unbalanced	ADJ
m-392	70	15	balanced	balanced	ADJ
m-392	70	16	k=3	k=3	X
m-392	70	17	n=5	n=5	ADV
m-392	70	18	0.032	0.032	NUM
m-392	70	19	0.060	0.060	NUM
m-392	70	20	n=10	n=10	NOUN
m-392	70	21	0.105	0.105	NUM
m-392	70	22	0.078	0.078	NUM
m-392	70	23	n=50	n=50	ADJ
m-392	70	24	0.093	0.093	NUM
m-392	70	25	0.028	0.028	NUM
m-392	70	26	k=10	k=10	NOUN
m-392	70	27	n=5	n=5	ADJ
m-392	70	28	0.048	0.048	NUM
m-392	70	29	0.034	0.034	NUM
m-392	70	30	n=10	n=10	NOUN
m-392	70	31	0.043	0.043	NUM
m-392	70	32	0.046	0.046	NUM
m-392	70	33	n=50	n=50	PROPN
m-392	70	34	0.076	0.076	NUM
m-392	70	35	0.044	0.044	NUM
m-392	70	36	k=50	k=50	PROPN
m-392	70	37	n=5	n=5	PROPN
m-392	70	38	0.042	0.042	NUM
m-392	70	39	0.035	0.035	NUM
m-392	70	40	n=10	n=10	NOUN
m-392	70	41	0.040	0.040	NUM
m-392	70	42	0.058	0.058	NUM
m-392	70	43	n=50	n=50	ADJ
m-392	70	44	0.039	0.039	NUM
m-392	70	45	0.030	0.030	NUM
m-392	70	46	fig	fig	NOUN
m-392	70	47	.	.	PUNCT
m-392	71	1	(	(	PUNCT
m-392	71	2	1	1	NUM
m-392	71	3	):	):	PUNCT
m-392	71	4	type	type	NOUN
m-392	71	5	-	-	PUNCT
m-392	71	6	i	i	PRON
m-392	71	7	error	error	NOUN
m-392	71	8	for	for	ADP
m-392	71	9	poisson	poisson	NOUN
m-392	71	10	gamma	gamma	NOUN
m-392	71	11	0	0	NUM
m-392	71	12	0.02	0.02	NUM
m-392	71	13	0.04	0.04	NUM
m-392	71	14	0.06	0.06	NUM
m-392	71	15	0.08	0.08	NUM
m-392	71	16	0.1	0.1	NUM
m-392	71	17	0.12	0.12	NUM
m-392	71	18	n=5	n=5	ADJ
m-392	71	19	n=10	n=10	NOUN
m-392	71	20	n=50	n=50	ADJ
m-392	71	21	n=5	n=5	ADJ
m-392	71	22	n=10	n=10	NOUN
m-392	71	23	n=50	n=50	ADJ
m-392	71	24	n=5	n=5	ADJ
m-392	71	25	n=10	n=10	NOUN
m-392	71	26	n=50	n=50	ADJ
m-392	71	27	unbalanced	unbalanced	ADJ
m-392	71	28	balanced	balanced	ADJ
m-392	71	29	ijo	ijo	PROPN
m-392	71	30	international	international	PROPN
m-392	71	31	journal	journal	PROPN
m-392	71	32	of	of	ADP
m-392	71	33	mathematics	mathematics	PROPN
m-392	71	34	volume	volume	PROPN
m-392	71	35	3|	3|	NUM
m-392	71	36	issue	issue	NOUN
m-392	71	37	12|	12|	NUM
m-392	71	38	december	december	PROPN
m-392	71	39	|	|	NOUN
m-392	71	40	2020	2020	NUM
m-392	71	41	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	71	42	22	22	NUM
m-392	71	43	the	the	DET
m-392	71	44	probability	probability	NOUN
m-392	71	45	of	of	ADP
m-392	71	46	type	type	NOUN
m-392	71	47	-	-	PUNCT
m-392	71	48	i	i	NOUN
m-392	71	49	error	error	NOUN
m-392	71	50	rate	rate	NOUN
m-392	71	51	was	be	AUX
m-392	71	52	acceptable	acceptable	ADJ
m-392	71	53	because	because	SCONJ
m-392	71	54	it	it	PRON
m-392	71	55	was	be	AUX
m-392	71	56	slightly	slightly	ADV
m-392	71	57	high	high	ADJ
m-392	71	58	in	in	ADP
m-392	71	59	some	some	DET
m-392	71	60	points	point	NOUN
m-392	71	61	;	;	PUNCT
m-392	71	62	generally	generally	ADV
m-392	71	63	it	it	PRON
m-392	71	64	was	be	AUX
m-392	71	65	not	not	PART
m-392	71	66	far	far	ADV
m-392	71	67	away	away	ADV
m-392	71	68	0.05	0.05	NUM
m-392	71	69	.	.	PUNCT
m-392	72	1	next	next	ADJ
m-392	72	2	table	table	NOUN
m-392	72	3	is	be	AUX
m-392	72	4	table(2	table(2	PROPN
m-392	72	5	)	)	PUNCT
m-392	72	6	;	;	PUNCT
m-392	72	7	for	for	ADP
m-392	72	8	the	the	DET
m-392	72	9	simulated	simulated	ADJ
m-392	72	10	sample	sample	NOUN
m-392	72	11	of	of	ADP
m-392	72	12	size	size	NOUN
m-392	72	13	(	(	PUNCT
m-392	72	14	5,10,50	5,10,50	NUM
m-392	72	15	)	)	PUNCT
m-392	72	16	observations	observation	NOUN
m-392	72	17	and	and	CCONJ
m-392	72	18	3	3	NUM
m-392	72	19	,	,	PUNCT
m-392	72	20	10	10	NUM
m-392	72	21	,	,	PUNCT
m-392	72	22	and	and	CCONJ
m-392	72	23	50	50	NUM
m-392	72	24	clusters	cluster	NOUN
m-392	72	25	;	;	PUNCT
m-392	72	26	where	where	SCONJ
m-392	72	27	the	the	DET
m-392	72	28	actual	actual	ADJ
m-392	72	29	value	value	NOUN
m-392	72	30	is	be	AUX
m-392	72	31	equal	equal	ADJ
m-392	72	32	to	to	ADP
m-392	72	33	0.2	0.2	NUM
m-392	72	34	for	for	ADP
m-392	72	35	the	the	DET
m-392	72	36	parameter	parameter	PROPN
m-392	72	37	�	�	PROPN
m-392	72	38	�	�	PROPN
m-392	72	39	,	,	PUNCT
m-392	72	40	and	and	CCONJ
m-392	72	41	the	the	DET
m-392	72	42	value	value	NOUN
m-392	72	43	for	for	ADP
m-392	72	44	the	the	DET
m-392	72	45	�	�	PROPN
m-392	72	46	�	�	PROPN
m-392	72	47	parameter	parameter	NOUN
m-392	72	48	is	be	AUX
m-392	72	49	equal	equal	ADJ
m-392	72	50	to	to	ADP
m-392	72	51	zero	zero	NUM
m-392	72	52	"	"	PUNCT
m-392	72	53	because	because	SCONJ
m-392	72	54	there	there	PRON
m-392	72	55	is	be	VERB
m-392	72	56	no	no	DET
m-392	72	57	x2	x2	ADJ
m-392	72	58	value	value	NOUN
m-392	72	59	,	,	PUNCT
m-392	72	60	it	it	PRON
m-392	72	61	is	be	AUX
m-392	72	62	used	use	VERB
m-392	72	63	only	only	ADV
m-392	72	64	to	to	PART
m-392	72	65	calculate	calculate	VERB
m-392	72	66	the	the	DET
m-392	72	67	power	power	NOUN
m-392	72	68	and	and	CCONJ
m-392	72	69	the	the	DET
m-392	72	70	probability	probability	NOUN
m-392	72	71	of	of	ADP
m-392	72	72	type	type	NOUN
m-392	72	73	-	-	PUNCT
m-392	72	74	i	i	NOUN
m-392	72	75	error	error	NOUN
m-392	72	76	rate	rate	NOUN
m-392	72	77	”	"	PUNCT
m-392	72	78	table	table	NOUN
m-392	72	79	(	(	PUNCT
m-392	72	80	2	2	NUM
m-392	72	81	):	):	PUNCT
m-392	72	82	the	the	DET
m-392	72	83	estimate	estimate	NOUN
m-392	72	84	parameters	parameter	NOUN
m-392	72	85	cluster	cluster	NOUN
m-392	72	86	observations	observation	NOUN
m-392	72	87	unbalanc	unbalanc	VERB
m-392	72	88	ed	ed	PROPN
m-392	72	89	balanced	balanced	PROPN
m-392	72	90	�	�	PROPN
m-392	72	91	�	�	PROPN
m-392	72	92	�	�	PROPN
m-392	72	93	�	�	PROPN
m-392	72	94	�	�	PROPN
m-392	72	95	�	�	PROPN
m-392	72	96	�	�	PROPN
m-392	72	97	�	�	PROPN
m-392	72	98	�	�	PROPN
m-392	72	99	�	�	PROPN
m-392	72	100	�	�	PROPN
m-392	72	101	�	�	PROPN
m-392	72	102	k=3	k=3	X
m-392	72	103	n=5	n=5	PRON
m-392	72	104	n=10	n=10	AUX
m-392	72	105	n=50	n=50	ADJ
m-392	72	106	0.2033534	0.2033534	NUM
m-392	72	107	0.1947158	0.1947158	NUM
m-392	72	108	0.2008493	0.2008493	NUM
m-392	72	109	0.003756905	0.003756905	NUM
m-392	72	110	-0.00445098	-0.00445098	NOUN
m-392	73	1	0.004040149	0.004040149	NUM
m-392	73	2	0.2041509	0.2041509	NUM
m-392	73	3	0.2028573	0.2028573	NUM
m-392	73	4	0.2045826	0.2045826	NUM
m-392	73	5	-0.00518115	-0.00518115	NOUN
m-392	73	6	0.01137586	0.01137586	NUM
m-392	73	7	0.00065012	0.00065012	NUM
m-392	73	8	k=10	k=10	NOUN
m-392	73	9	n=5	n=5	PRON
m-392	73	10	n=10	n=10	AUX
m-392	73	11	n=50	n=50	PROPN
m-392	73	12	0.1994582	0.1994582	NUM
m-392	73	13	0.1980559	0.1980559	NUM
m-392	73	14	0.2008564	0.2008564	NUM
m-392	73	15	-0.00049512	-0.00049512	NOUN
m-392	73	16	-0.00192974	-0.00192974	NOUN
m-392	73	17	-0.00039103	-0.00039103	NOUN
m-392	74	1	0.2017183	0.2017183	NUM
m-392	74	2	0.2022803	0.2022803	NUM
m-392	74	3	0.1994892	0.1994892	NUM
m-392	74	4	-0.00151689	-0.00151689	NOUN
m-392	74	5	3.240238e-05	3.240238e-05	NUM
m-392	74	6	-0.00093368	-0.00093368	NOUN
m-392	74	7	k=50	k=50	PROPN
m-392	74	8	n=5	n=5	X
m-392	74	9	n=10	n=10	NOUN
m-392	74	10	n=50	n=50	ADJ
m-392	74	11	0.2010042	0.2010042	NUM
m-392	74	12	0.1995827	0.1995827	NUM
m-392	74	13	0.1997564	0.1997564	NUM
m-392	74	14	0.0006905077	0.0006905077	NUM
m-392	74	15	0.00053609	0.00053609	NUM
m-392	74	16	1.147578e-05	1.147578e-05	NUM
m-392	74	17	0.1977052	0.1977052	NUM
m-392	74	18	0.2004524	0.2004524	NUM
m-392	74	19	0.2008468	0.2008468	NUM
m-392	74	20	0.0004996838	0.0004996838	NUM
m-392	74	21	-0.001911985	-0.001911985	NOUN
m-392	74	22	0.0002390147	0.0002390147	NUM
m-392	74	23	table	table	NOUN
m-392	74	24	(	(	PUNCT
m-392	74	25	2	2	NUM
m-392	74	26	)	)	PUNCT
m-392	74	27	shows	show	VERB
m-392	74	28	that	that	SCONJ
m-392	74	29	the	the	DET
m-392	74	30	h	h	NOUN
m-392	74	31	-	-	PUNCT
m-392	74	32	likelihood	likelihood	NOUN
m-392	74	33	estimate	estimate	NOUN
m-392	74	34	was	be	AUX
m-392	74	35	a	a	DET
m-392	74	36	good	good	ADJ
m-392	74	37	estimation	estimation	NOUN
m-392	74	38	method	method	NOUN
m-392	74	39	for	for	ADP
m-392	74	40	both	both	DET
m-392	74	41	cases	case	NOUN
m-392	74	42	,	,	PUNCT
m-392	74	43	since	since	SCONJ
m-392	74	44	the	the	DET
m-392	74	45	average	average	NOUN
m-392	74	46	of	of	ADP
m-392	74	47	1,000	1,000	NUM
m-392	74	48	replications	replication	NOUN
m-392	74	49	provided	provide	VERB
m-392	74	50	estimates	estimate	NOUN
m-392	74	51	that	that	PRON
m-392	74	52	were	be	AUX
m-392	74	53	very	very	ADV
m-392	74	54	close	close	ADJ
m-392	74	55	to	to	ADP
m-392	74	56	the	the	DET
m-392	74	57	actual	actual	ADJ
m-392	74	58	values	value	NOUN
m-392	74	59	for	for	ADP
m-392	74	60	the	the	DET
m-392	74	61	parameters	parameter	NOUN
m-392	74	62	.	.	PUNCT
m-392	75	1	figs	fig	NOUN
m-392	75	2	2.1	2.1	NUM
m-392	75	3	and	and	CCONJ
m-392	75	4	2.2	2.2	NUM
m-392	75	5	included	include	VERB
m-392	75	6	a	a	DET
m-392	75	7	summary	summary	NOUN
m-392	75	8	of	of	ADP
m-392	75	9	the	the	DET
m-392	75	10	predicted	predict	VERB
m-392	75	11	values	value	NOUN
m-392	75	12	that	that	PRON
m-392	75	13	were	be	AUX
m-392	75	14	close	close	ADJ
m-392	75	15	to	to	ADP
m-392	75	16	the	the	DET
m-392	75	17	actual	actual	ADJ
m-392	75	18	values	value	NOUN
m-392	75	19	.	.	PUNCT
m-392	76	1	fig(2.1	fig(2.1	NUM
m-392	76	2	):	):	PUNCT
m-392	76	3	estimate	estimate	NOUN
m-392	76	4	values	value	NOUN
m-392	76	5	(	(	PUNCT
m-392	76	6	�	�	PROPN
m-392	76	7	�	�	PROPN
m-392	76	8	�	�	PROPN
m-392	76	9	)	)	PUNCT
m-392	76	10	fig(2.2	fig(2.2	NUM
m-392	76	11	):	):	PUNCT
m-392	76	12	estimate	estimate	NOUN
m-392	76	13	values	value	NOUN
m-392	76	14	(	(	PUNCT
m-392	76	15	�	�	PROPN
m-392	76	16	�	�	PROPN
m-392	76	17	�	�	PROPN
m-392	76	18	)	)	PUNCT
m-392	76	19	0.188	0.188	NUM
m-392	76	20	0.19	0.19	NUM
m-392	76	21	0.192	0.192	NUM
m-392	76	22	0.194	0.194	NUM
m-392	76	23	0.196	0.196	NUM
m-392	76	24	0.198	0.198	NUM
m-392	76	25	0.2	0.2	NUM
m-392	76	26	0.202	0.202	NUM
m-392	76	27	0.204	0.204	NUM
m-392	76	28	0.206	0.206	NUM
m-392	76	29	n=5	n=5	ADJ
m-392	76	30	n=10n=50	n=10n=50	ADJ
m-392	76	31	n=5	n=5	ADJ
m-392	76	32	n=10n=50	n=10n=50	ADJ
m-392	76	33	n=5	n=5	ADJ
m-392	76	34	n=10n=50	n=10n=50	ADJ
m-392	76	35	balanced	balance	VERB
m-392	76	36	unbalanced	unbalanced	ADJ
m-392	76	37	-0.01	-0.01	NUM
m-392	76	38	-0.005	-0.005	SYM
m-392	76	39	0	0	NUM
m-392	76	40	0.005	0.005	NUM
m-392	76	41	0.01	0.01	NUM
m-392	76	42	0.015	0.015	NUM
m-392	76	43	n=5	n=5	PRON
m-392	76	44	n=10	n=10	NOUN
m-392	76	45	n=50	n=50	ADJ
m-392	76	46	n=5	n=5	ADJ
m-392	76	47	n=10	n=10	NOUN
m-392	76	48	n=50	n=50	ADJ
m-392	76	49	n=5	n=5	ADJ
m-392	76	50	n=10	n=10	PRON
m-392	76	51	n=50	n=50	ADJ
m-392	76	52	balancd	balancd	VERB
m-392	76	53	unbalanced	unbalanced	ADJ
m-392	76	54	ijo	ijo	PROPN
m-392	76	55	international	international	PROPN
m-392	76	56	journal	journal	PROPN
m-392	76	57	of	of	ADP
m-392	76	58	mathematics	mathematics	PROPN
m-392	76	59	volume	volume	PROPN
m-392	76	60	3|	3|	NUM
m-392	76	61	issue	issue	NOUN
m-392	76	62	12|	12|	NUM
m-392	76	63	december	december	PROPN
m-392	76	64	|	|	NOUN
m-392	76	65	2020	2020	NUM
m-392	76	66	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	76	67	23	23	NUM
m-392	76	68	next	next	ADJ
m-392	76	69	table	table	NOUN
m-392	76	70	(	(	PUNCT
m-392	76	71	3	3	X
m-392	76	72	)	)	PUNCT
m-392	76	73	demonstrate	demonstrate	VERB
m-392	76	74	the	the	DET
m-392	76	75	power	power	NOUN
m-392	76	76	simulated	simulate	VERB
m-392	76	77	sample	sample	NOUN
m-392	76	78	of	of	ADP
m-392	76	79	size	size	NOUN
m-392	76	80	5,10	5,10	NUM
m-392	76	81	,	,	PUNCT
m-392	76	82	and	and	CCONJ
m-392	76	83	50	50	NUM
m-392	76	84	observations	observation	NOUN
m-392	76	85	and	and	CCONJ
m-392	76	86	the	the	DET
m-392	76	87	number	number	NOUN
m-392	76	88	of	of	ADP
m-392	76	89	:	:	PUNCT
m-392	76	90	3	3	NUM
m-392	76	91	,	,	PUNCT
m-392	76	92	10	10	NUM
m-392	76	93	,	,	PUNCT
m-392	76	94	and	and	CCONJ
m-392	76	95	50	50	NUM
m-392	76	96	clusters	cluster	NOUN
m-392	76	97	.	.	PUNCT
m-392	77	1	statistical	statistical	ADJ
m-392	77	2	power	power	NOUN
m-392	77	3	was	be	AUX
m-392	77	4	computed	compute	VERB
m-392	77	5	when	when	SCONJ
m-392	77	6	rejected	reject	VERB
m-392	77	7	hypothesis	hypothesis	NOUN
m-392	77	8	�	�	PROPN
m-392	77	9	�	�	PROPN
m-392	77	10	:	:	PUNCT
m-392	77	11	�	�	PROPN
m-392	77	12	�	�	PROPN
m-392	77	13	=	=	SYM
m-392	77	14	0	0	NUM
m-392	77	15	,	,	PUNCT
m-392	77	16	correctly	correctly	ADV
m-392	77	17	.	.	PUNCT
m-392	78	1	calculate	calculate	VERB
m-392	78	2	through	through	ADP
m-392	78	3	simulation	simulation	NOUN
m-392	78	4	for	for	ADP
m-392	78	5	1000	1000	NUM
m-392	78	6	times	time	NOUN
m-392	78	7	how	how	SCONJ
m-392	78	8	many	many	ADJ
m-392	78	9	times	time	NOUN
m-392	78	10	the	the	DET
m-392	78	11	test	test	NOUN
m-392	78	12	is	be	AUX
m-392	78	13	significant	significant	ADJ
m-392	78	14	.	.	PUNCT
m-392	79	1	the	the	DET
m-392	79	2	power	power	NOUN
m-392	79	3	is	be	AUX
m-392	79	4	the	the	DET
m-392	79	5	proportion	proportion	NOUN
m-392	79	6	of	of	ADP
m-392	79	7	number	number	NOUN
m-392	79	8	of	of	ADP
m-392	79	9	rejected	reject	VERB
m-392	79	10	correctly	correctly	ADV
m-392	79	11	.	.	PUNCT
m-392	80	1	table	table	NOUN
m-392	80	2	(	(	PUNCT
m-392	80	3	3	3	X
m-392	80	4	)	)	PUNCT
m-392	80	5	statistic	statistic	ADJ
m-392	80	6	power	power	NOUN
m-392	80	7	cluster	cluster	NOUN
m-392	80	8	observations	observation	NOUN
m-392	80	9	unbalanced	unbalanced	ADJ
m-392	80	10	balanced	balanced	ADJ
m-392	80	11	k=3	k=3	X
m-392	80	12	n=5	n=5	PRON
m-392	80	13	n=10	n=10	AUX
m-392	80	14	n=50	n=50	ADJ
m-392	80	15	0.801	0.801	NUM
m-392	80	16	0.963	0.963	NUM
m-392	80	17	1.000	1.000	NUM
m-392	80	18	0.808	0.808	NUM
m-392	80	19	1.000	1.000	NUM
m-392	80	20	1.000	1.000	NUM
m-392	80	21	k=10	k=10	NOUN
m-392	80	22	n=5	n=5	PRON
m-392	80	23	n=10	n=10	AUX
m-392	80	24	n=50	n=50	ADJ
m-392	80	25	1.000	1.000	NUM
m-392	80	26	1.000	1.000	NUM
m-392	80	27	1.000	1.000	NUM
m-392	80	28	1.000	1.000	NUM
m-392	80	29	1.000	1.000	NUM
m-392	80	30	1.000	1.000	NUM
m-392	80	31	k=50	k=50	PROPN
m-392	80	32	n=5	n=5	PRON
m-392	80	33	n=10	n=10	NOUN
m-392	80	34	n=50	n=50	ADJ
m-392	80	35	1.000	1.000	NUM
m-392	80	36	1.000	1.000	NUM
m-392	80	37	1.000	1.000	NUM
m-392	80	38	1.000	1.000	NUM
m-392	80	39	1.000	1.000	NUM
m-392	80	40	1.000	1.000	NUM
m-392	80	41	from	from	ADP
m-392	80	42	table	table	NOUN
m-392	80	43	(	(	PUNCT
m-392	80	44	3	3	X
m-392	80	45	)	)	PUNCT
m-392	80	46	it	it	PRON
m-392	80	47	has	have	AUX
m-392	80	48	been	be	AUX
m-392	80	49	shown	show	VERB
m-392	80	50	that	that	SCONJ
m-392	80	51	the	the	DET
m-392	80	52	power	power	NOUN
m-392	80	53	values	value	NOUN
m-392	80	54	of	of	ADP
m-392	80	55	"	"	PUNCT
m-392	80	56	probability	probability	NOUN
m-392	80	57	to	to	PART
m-392	80	58	accept	accept	VERB
m-392	80	59	a	a	DET
m-392	80	60	null	null	ADJ
m-392	80	61	hypothesis	hypothesis	NOUN
m-392	80	62	that	that	PRON
m-392	80	63	is	be	AUX
m-392	80	64	right	right	ADJ
m-392	80	65	"	"	PUNCT
m-392	80	66	are	be	AUX
m-392	80	67	approximately	approximately	ADV
m-392	80	68	close	close	ADJ
m-392	80	69	to	to	ADP
m-392	80	70	one	one	NUM
m-392	80	71	.	.	PUNCT
m-392	81	1	the	the	DET
m-392	81	2	higher	high	ADJ
m-392	81	3	power	power	NOUN
m-392	81	4	the	the	DET
m-392	81	5	better	well	ADJ
m-392	81	6	method	method	NOUN
m-392	81	7	,	,	PUNCT
m-392	81	8	from	from	ADP
m-392	81	9	the	the	DET
m-392	81	10	above	above	ADJ
m-392	81	11	table	table	NOUN
m-392	81	12	hard	hard	ADV
m-392	81	13	to	to	PART
m-392	81	14	decide	decide	VERB
m-392	81	15	since	since	SCONJ
m-392	81	16	the	the	DET
m-392	81	17	power	power	NOUN
m-392	81	18	approximately	approximately	ADV
m-392	81	19	is	be	AUX
m-392	81	20	1	1	NUM
m-392	81	21	,	,	PUNCT
m-392	81	22	and	and	CCONJ
m-392	81	23	is	be	AUX
m-392	81	24	high	high	ADJ
m-392	81	25	for	for	ADP
m-392	81	26	both	both	DET
m-392	81	27	cases	case	NOUN
m-392	81	28	;	;	PUNCT
m-392	81	29	since	since	SCONJ
m-392	81	30	the	the	DET
m-392	81	31	sample	sample	NOUN
m-392	81	32	size	size	NOUN
m-392	81	33	is	be	AUX
m-392	81	34	large	large	ADJ
m-392	81	35	for	for	ADP
m-392	81	36	each	each	DET
m-392	81	37	combination	combination	NOUN
m-392	81	38	.	.	PUNCT
m-392	82	1	it	it	PRON
m-392	82	2	is	be	AUX
m-392	82	3	reasonable	reasonable	ADJ
m-392	82	4	high	high	ADJ
m-392	82	5	power	power	NOUN
m-392	82	6	for	for	ADP
m-392	82	7	large	large	ADJ
m-392	82	8	sample	sample	NOUN
m-392	82	9	size	size	NOUN
m-392	82	10	,	,	PUNCT
m-392	82	11	there	there	PRON
m-392	82	12	is	be	VERB
m-392	82	13	no	no	ADV
m-392	82	14	different	different	ADJ
m-392	82	15	between	between	ADP
m-392	82	16	both	both	DET
m-392	82	17	cases	case	NOUN
m-392	82	18	in	in	ADP
m-392	82	19	power	power	NOUN
m-392	82	20	,	,	PUNCT
m-392	82	21	both	both	PRON
m-392	82	22	work	work	VERB
m-392	82	23	good	good	ADJ
m-392	82	24	according	accord	VERB
m-392	82	25	to	to	ADP
m-392	82	26	power	power	NOUN
m-392	82	27	for	for	ADP
m-392	82	28	large	large	ADJ
m-392	82	29	sample	sample	NOUN
m-392	82	30	size	size	NOUN
m-392	82	31	.	.	PUNCT
m-392	83	1	table	table	NOUN
m-392	83	2	(	(	PUNCT
m-392	83	3	4	4	NUM
m-392	83	4	):	):	PUNCT
m-392	83	5	stander	stander	NOUN
m-392	83	6	error	error	NOUN
m-392	83	7	(	(	PUNCT
m-392	83	8	se	se	X
m-392	83	9	)	)	PUNCT
m-392	83	10	for	for	ADP
m-392	83	11	original	original	ADJ
m-392	83	12	simulated	simulate	VERB
m-392	83	13	sample	sample	NOUN
m-392	83	14	of	of	ADP
m-392	83	15	size	size	NOUN
m-392	83	16	5,10	5,10	NUM
m-392	83	17	,	,	PUNCT
m-392	83	18	and	and	CCONJ
m-392	83	19	50	50	NUM
m-392	83	20	observations	observation	NOUN
m-392	83	21	and	and	CCONJ
m-392	83	22	3,10	3,10	NUM
m-392	83	23	,	,	PUNCT
m-392	83	24	and	and	CCONJ
m-392	83	25	50	50	NUM
m-392	83	26	clusters	cluster	NOUN
m-392	83	27	.	.	PUNCT
m-392	84	1	the	the	DET
m-392	84	2	stander	stander	NOUN
m-392	84	3	error	error	NOUN
m-392	84	4	was	be	AUX
m-392	84	5	computed	compute	VERB
m-392	84	6	as	as	ADP
m-392	84	7	the	the	DET
m-392	84	8	average	average	NOUN
m-392	84	9	of	of	ADP
m-392	84	10	1000	1000	NUM
m-392	84	11	ses	se	NOUN
m-392	84	12	of	of	ADP
m-392	84	13	the	the	DET
m-392	84	14	estimates	estimate	NOUN
m-392	84	15	of	of	ADP
m-392	84	16	�	�	PROPN
m-392	84	17	�	�	PROPN
m-392	84	18	.	.	PUNCT
m-392	85	1	the	the	DET
m-392	85	2	smaller	small	ADJ
m-392	85	3	se	se	X
m-392	85	4	represents	represent	VERB
m-392	85	5	smaller	small	ADJ
m-392	85	6	variability	variability	NOUN
m-392	85	7	,	,	PUNCT
m-392	85	8	or	or	CCONJ
m-392	85	9	greater	great	ADJ
m-392	85	10	precision	precision	NOUN
m-392	85	11	,	,	PUNCT
m-392	85	12	of	of	ADP
m-392	85	13	the	the	DET
m-392	85	14	parameter	parameter	NOUN
m-392	85	15	estimates	estimate	NOUN
m-392	85	16	(	(	PUNCT
m-392	85	17	heo	heo	PROPN
m-392	85	18	and	and	CCONJ
m-392	85	19	leon	leon	PROPN
m-392	85	20	,	,	PUNCT
m-392	85	21	2005	2005	NUM
m-392	85	22	)	)	PUNCT
m-392	85	23	.	.	PUNCT
m-392	86	1	table	table	NOUN
m-392	86	2	(	(	PUNCT
m-392	86	3	4	4	NUM
m-392	86	4	):	):	PUNCT
m-392	86	5	stander	stander	NOUN
m-392	86	6	error	error	NOUN
m-392	86	7	for	for	ADP
m-392	86	8	both	both	DET
m-392	86	9	cases	case	NOUN
m-392	86	10	counting	count	VERB
m-392	86	11	data	datum	NOUN
m-392	86	12	fig.(4	fig.(4	PROPN
m-392	86	13	)	)	PUNCT
m-392	86	14	standard	standard	ADJ
m-392	86	15	error	error	NOUN
m-392	86	16	for	for	ADP
m-392	86	17	balanced	balanced	ADJ
m-392	86	18	and	and	CCONJ
m-392	86	19	unbalanced	unbalanced	ADJ
m-392	86	20	data	datum	NOUN
m-392	86	21	.	.	PUNCT
m-392	87	1	cluster	cluster	NOUN
m-392	87	2	observations	observation	NOUN
m-392	87	3	unbalanced	unbalanced	ADJ
m-392	87	4	balanced	balanced	ADJ
m-392	87	5	k=3	k=3	X
m-392	87	6	n=5	n=5	PRON
m-392	87	7	n=10	n=10	X
m-392	87	8	n=50	n=50	PROPN
m-392	87	9	0.07196653	0.07196653	NUM
m-392	87	10	0.04438624	0.04438624	NUM
m-392	87	11	0.01834041	0.01834041	NUM
m-392	87	12	0.06844161	0.06844161	NUM
m-392	87	13	0.04374402	0.04374402	NUM
m-392	87	14	0.01862745	0.01862745	NUM
m-392	87	15	k=10	k=10	NOUN
m-392	87	16	n=5	n=5	PRON
m-392	87	17	n=10	n=10	PROPN
m-392	87	18	n=50	n=50	ADJ
m-392	87	19	0.03354806	0.03354806	NUM
m-392	87	20	0.02272336	0.02272336	NUM
m-392	87	21	0.00991545	0.00991545	NUM
m-392	87	22	0.03383271	0.03383271	NUM
m-392	87	23	0.02271749	0.02271749	NUM
m-392	87	24	0.00993959	0.00993959	NUM
m-392	87	25	k=50	k=50	PROPN
m-392	87	26	n=5	n=5	PRON
m-392	87	27	n=10	n=10	NOUN
m-392	87	28	n=50	n=50	ADJ
m-392	87	29	0.01423205	0.01423205	NUM
m-392	87	30	0.00995692	0.00995692	NUM
m-392	87	31	0.00443169	0.00443169	NUM
m-392	87	32	0.01417949	0.01417949	NUM
m-392	87	33	0.00992440	0.00992440	NUM
m-392	87	34	0.00443549	0.00443549	NUM
m-392	87	35	ijo	ijo	PROPN
m-392	87	36	international	international	PROPN
m-392	87	37	journal	journal	PROPN
m-392	87	38	of	of	ADP
m-392	87	39	mathematics	mathematics	PROPN
m-392	87	40	volume	volume	PROPN
m-392	87	41	3|	3|	NUM
m-392	87	42	issue	issue	NOUN
m-392	87	43	12|	12|	NUM
m-392	87	44	december	december	PROPN
m-392	87	45	|	|	NOUN
m-392	87	46	2020	2020	NUM
m-392	87	47	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	NOUN
m-392	87	48	24	24	NUM
m-392	87	49	from	from	ADP
m-392	87	50	table	table	NOUN
m-392	87	51	(	(	PUNCT
m-392	87	52	4	4	NUM
m-392	87	53	)	)	PUNCT
m-392	87	54	and	and	CCONJ
m-392	87	55	graph	graph	NOUN
m-392	87	56	(	(	PUNCT
m-392	87	57	4	4	NUM
m-392	87	58	)	)	PUNCT
m-392	87	59	above	above	ADV
m-392	87	60	,	,	PUNCT
m-392	87	61	it	it	PRON
m-392	87	62	can	can	AUX
m-392	87	63	be	be	AUX
m-392	87	64	seen	see	VERB
m-392	87	65	that	that	SCONJ
m-392	87	66	there	there	PRON
m-392	87	67	is	be	VERB
m-392	87	68	no	no	DET
m-392	87	69	difference	difference	NOUN
m-392	87	70	in	in	ADP
m-392	87	71	the	the	DET
m-392	87	72	standard	standard	ADJ
m-392	87	73	error	error	NOUN
m-392	87	74	for	for	ADP
m-392	87	75	hglm	hglm	NOUN
m-392	87	76	poisson	poisson	NOUN
m-392	87	77	game	game	NOUN
m-392	87	78	in	in	ADP
m-392	87	79	balanced	balanced	ADJ
m-392	87	80	and	and	CCONJ
m-392	87	81	unbalanced	unbalanced	ADJ
m-392	87	82	counting	counting	NOUN
m-392	87	83	data	datum	NOUN
m-392	87	84	discussion	discussion	NOUN
m-392	87	85	in	in	ADP
m-392	87	86	this	this	DET
m-392	87	87	article	article	NOUN
m-392	87	88	,	,	PUNCT
m-392	87	89	we	we	PRON
m-392	87	90	looked	look	VERB
m-392	87	91	at	at	ADP
m-392	87	92	the	the	DET
m-392	87	93	generalized	generalized	ADJ
m-392	87	94	linear	linear	ADJ
m-392	87	95	mixed	mix	VERB
m-392	87	96	-	-	PUNCT
m-392	87	97	models	model	NOUN
m-392	87	98	,	,	PUNCT
m-392	87	99	which	which	PRON
m-392	87	100	are	be	AUX
m-392	87	101	the	the	DET
m-392	87	102	extension	extension	NOUN
m-392	87	103	of	of	ADP
m-392	87	104	linear	linear	PROPN
m-392	87	105	models	model	NOUN
m-392	87	106	.	.	PUNCT
m-392	88	1	it	it	PRON
m-392	88	2	is	be	AUX
m-392	88	3	understood	understand	VERB
m-392	88	4	that	that	SCONJ
m-392	88	5	many	many	ADJ
m-392	88	6	other	other	ADJ
m-392	88	7	studies	study	NOUN
m-392	88	8	have	have	AUX
m-392	88	9	studied	study	VERB
m-392	88	10	a	a	DET
m-392	88	11	problem	problem	NOUN
m-392	88	12	with	with	ADP
m-392	88	13	unbalanced	unbalanced	ADJ
m-392	88	14	data	datum	NOUN
m-392	88	15	or	or	CCONJ
m-392	88	16	incomplete	incomplete	ADJ
m-392	88	17	information	information	NOUN
m-392	88	18	,	,	PUNCT
m-392	88	19	which	which	PRON
m-392	88	20	may	may	AUX
m-392	88	21	lead	lead	VERB
m-392	88	22	to	to	ADP
m-392	88	23	a	a	DET
m-392	88	24	heterogenetic	heterogenetic	ADJ
m-392	88	25	problem	problem	NOUN
m-392	88	26	.	.	PUNCT
m-392	89	1	the	the	DET
m-392	89	2	heterogenetic	heterogenetic	ADJ
m-392	89	3	issue	issue	NOUN
m-392	89	4	was	be	AUX
m-392	89	5	not	not	PART
m-392	89	6	discussed	discuss	VERB
m-392	89	7	here	here	ADV
m-392	89	8	by	by	ADP
m-392	89	9	the	the	DET
m-392	89	10	use	use	NOUN
m-392	89	11	of	of	ADP
m-392	89	12	the	the	DET
m-392	89	13	hierarchical	hierarchical	ADJ
m-392	89	14	probability	probability	NOUN
m-392	89	15	estimation	estimation	NOUN
m-392	89	16	model	model	NOUN
m-392	89	17	.	.	PUNCT
m-392	90	1	the	the	DET
m-392	90	2	process	process	NOUN
m-392	90	3	has	have	VERB
m-392	90	4	impartial	impartial	ADJ
m-392	90	5	and	and	CCONJ
m-392	90	6	very	very	ADV
m-392	90	7	similar	similar	ADJ
m-392	90	8	outcomes	outcome	NOUN
m-392	90	9	in	in	ADP
m-392	90	10	two	two	NUM
m-392	90	11	situations	situation	NOUN
m-392	90	12	that	that	PRON
m-392	90	13	are	be	AUX
m-392	90	14	balanced	balanced	ADJ
m-392	90	15	and	and	CCONJ
m-392	90	16	unbalanced	unbalanced	ADJ
m-392	90	17	.	.	PUNCT
m-392	91	1	the	the	DET
m-392	91	2	lack	lack	NOUN
m-392	91	3	of	of	ADP
m-392	91	4	meaning	meaning	NOUN
m-392	91	5	and	and	CCONJ
m-392	91	6	the	the	DET
m-392	91	7	imbalanced	imbalanced	ADJ
m-392	91	8	model	model	NOUN
m-392	91	9	will	will	AUX
m-392	91	10	therefore	therefore	ADV
m-392	91	11	not	not	PART
m-392	91	12	be	be	AUX
m-392	91	13	a	a	DET
m-392	91	14	concern	concern	NOUN
m-392	91	15	by	by	ADP
m-392	91	16	using	use	VERB
m-392	91	17	the	the	DET
m-392	91	18	poisson	poisson	NOUN
m-392	91	19	-	-	PUNCT
m-392	91	20	gamma	gamma	NOUN
m-392	91	21	h	h	NOUN
m-392	91	22	-	-	PUNCT
m-392	91	23	likelihood	likelihood	NOUN
m-392	91	24	estimation	estimation	NOUN
m-392	91	25	.	.	PUNCT
m-392	92	1	the	the	DET
m-392	92	2	hierarchical	hierarchical	ADJ
m-392	92	3	probability	probability	NOUN
m-392	92	4	estimation	estimation	NOUN
m-392	92	5	approach	approach	NOUN
m-392	92	6	has	have	AUX
m-392	92	7	been	be	AUX
m-392	92	8	concluded	conclude	VERB
m-392	92	9	to	to	PART
m-392	92	10	be	be	AUX
m-392	92	11	able	able	ADJ
m-392	92	12	to	to	PART
m-392	92	13	solve	solve	VERB
m-392	92	14	heterogenetic	heterogenetic	ADJ
m-392	92	15	problems	problem	NOUN
m-392	92	16	in	in	ADP
m-392	92	17	future	future	ADJ
m-392	92	18	studies	study	NOUN
m-392	92	19	.	.	PUNCT
m-392	93	1	as	as	SCONJ
m-392	93	2	stated	state	VERB
m-392	93	3	earlier	early	ADV
m-392	93	4	,	,	PUNCT
m-392	93	5	this	this	DET
m-392	93	6	study	study	NOUN
m-392	93	7	's	's	PART
m-392	93	8	main	main	ADJ
m-392	93	9	objective	objective	NOUN
m-392	93	10	was	be	AUX
m-392	93	11	the	the	DET
m-392	93	12	efficiency	efficiency	NOUN
m-392	93	13	of	of	ADP
m-392	93	14	the	the	DET
m-392	93	15	h	h	NOUN
m-392	93	16	-	-	PUNCT
m-392	93	17	likelihood	likelihood	NOUN
m-392	93	18	estimation	estimation	NOUN
m-392	93	19	approach	approach	NOUN
m-392	93	20	for	for	ADP
m-392	93	21	unbalanced	unbalanced	ADJ
m-392	93	22	cluster	cluster	NOUN
m-392	93	23	data	datum	NOUN
m-392	93	24	models	model	NOUN
m-392	93	25	.	.	PUNCT
m-392	94	1	h	h	NOUN
m-392	94	2	-	-	PUNCT
m-392	94	3	likelihood	likelihood	NOUN
m-392	94	4	estimation	estimation	NOUN
m-392	94	5	approach	approach	VERB
m-392	94	6	the	the	DET
m-392	94	7	system	system	NOUN
m-392	94	8	for	for	ADP
m-392	94	9	unbalanced	unbalanced	ADJ
m-392	94	10	clustered	clustered	ADJ
m-392	94	11	count	count	NOUN
m-392	94	12	data	datum	NOUN
m-392	94	13	models	model	NOUN
m-392	94	14	is	be	AUX
m-392	94	15	recommended	recommend	VERB
m-392	94	16	in	in	ADP
m-392	94	17	order	order	NOUN
m-392	94	18	to	to	PART
m-392	94	19	avoid	avoid	VERB
m-392	94	20	heterogeneity	heterogeneity	NOUN
m-392	94	21	problems	problem	NOUN
m-392	94	22	.	.	PUNCT
m-392	95	1	0	0	NUM
m-392	95	2	0.01	0.01	NUM
m-392	95	3	0.02	0.02	NUM
m-392	95	4	0.03	0.03	NUM
m-392	95	5	0.04	0.04	NUM
m-392	95	6	0.05	0.05	NUM
m-392	95	7	0.06	0.06	NUM
m-392	95	8	0.07	0.07	NUM
m-392	95	9	0.08	0.08	NUM
m-392	95	10	n=5	n=5	ADJ
m-392	95	11	n=10	n=10	NOUN
m-392	95	12	n=50	n=50	ADJ
m-392	95	13	n=5	n=5	ADJ
m-392	95	14	n=10	n=10	NOUN
m-392	95	15	n=50	n=50	ADJ
m-392	95	16	n=5	n=5	ADJ
m-392	95	17	n=10	n=10	NOUN
m-392	95	18	n=50	n=50	ADJ
m-392	95	19	unbalanced	unbalanced	ADJ
m-392	95	20	balanced	balanced	ADJ
m-392	95	21	ijo	ijo	PROPN
m-392	95	22	international	international	PROPN
m-392	95	23	journal	journal	PROPN
m-392	95	24	of	of	ADP
m-392	95	25	mathematics	mathematics	PROPN
m-392	95	26	volume	volume	PROPN
m-392	95	27	3|	3|	NUM
m-392	95	28	issue	issue	NOUN
m-392	95	29	12|	12|	NUM
m-392	95	30	december	december	PROPN
m-392	96	1	|	|	NOUN
m-392	96	2	2020	2020	NUM
m-392	96	3	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	96	4	25	25	NUM
m-392	96	5	references	reference	NOUN
m-392	96	6	el	el	PROPN
m-392	96	7	-	-	PROPN
m-392	96	8	saeiti	saeiti	PROPN
m-392	96	9	,	,	PUNCT
m-392	96	10	i.	i.	PROPN
m-392	96	11	n.	n.	PROPN
m-392	96	12	(	(	PUNCT
m-392	96	13	2014	2014	NUM
m-392	96	14	)	)	PUNCT
m-392	96	15	“	"	PUNCT
m-392	96	16	performance	performance	NOUN
m-392	96	17	of	of	ADP
m-392	96	18	mixed	mixed	ADJ
m-392	96	19	effects	effect	NOUN
m-392	96	20	for	for	ADP
m-392	96	21	clustered	clustered	ADJ
m-392	96	22	binary	binary	ADJ
m-392	96	23	data	datum	NOUN
m-392	96	24	models	model	NOUN
m-392	96	25	”	"	PUNCT
m-392	96	26	.	.	PUNCT
m-392	97	1	aip	aip	PROPN
m-392	97	2	conference	conference	NOUN
m-392	97	3	proceedings	proceeding	NOUN
m-392	97	4	1643	1643	NUM
m-392	97	5	,	,	PUNCT
m-392	97	6	80	80	NUM
m-392	97	7	el	el	PROPN
m-392	97	8	-	-	PUNCT
m-392	97	9	saeiti	saeiti	PROPN
m-392	97	10	,	,	PUNCT
m-392	97	11	i.	i.	PROPN
m-392	97	12	n.	n.	PROPN
m-392	97	13	(	(	PUNCT
m-392	97	14	2015	2015	NUM
m-392	97	15	)	)	PUNCT
m-392	97	16	.	.	PUNCT
m-392	98	1	"	"	PUNCT
m-392	98	2	messy	messy	ADJ
m-392	98	3	data	datum	NOUN
m-392	98	4	in	in	ADP
m-392	98	5	heteroscedastic	heteroscedastic	ADJ
m-392	98	6	models	model	NOUN
m-392	98	7	case	case	NOUN
m-392	98	8	study	study	NOUN
m-392	98	9	:	:	PUNCT
m-392	98	10	mixed	mixed	ADJ
m-392	98	11	nested	nested	ADJ
m-392	98	12	design	design	NOUN
m-392	98	13	"	"	PUNCT
m-392	98	14	.	.	PUNCT
m-392	99	1	lap	lap	PROPN
m-392	99	2	lambert	lambert	PROPN
m-392	99	3	academic	academic	ADJ
m-392	99	4	publishing	publishing	NOUN
m-392	99	5	.	.	PUNCT
m-392	100	1	el	el	PROPN
m-392	100	2	-	-	PUNCT
m-392	100	3	saeiti	saeiti	PROPN
m-392	100	4	,	,	PUNCT
m-392	100	5	i.	i.	PROPN
m-392	100	6	n.	n.	PROPN
m-392	100	7	(	(	PUNCT
m-392	100	8	2019	2019	NUM
m-392	100	9	)	)	PUNCT
m-392	100	10	.	.	PUNCT
m-392	101	1	an	an	DET
m-392	101	2	adjusted	adjust	VERB
m-392	101	3	scale	scale	NOUN
m-392	101	4	binomial	binomial	ADJ
m-392	101	5	beta	beta	ADJ
m-392	101	6	h	h	NOUN
m-392	101	7	-	-	PUNCT
m-392	101	8	likelihood	likelihood	NOUN
m-392	101	9	estimation	estimation	NOUN
m-392	101	10	method	method	NOUN
m-392	101	11	for	for	ADP
m-392	101	12	unbalanced	unbalanced	ADJ
m-392	101	13	clus	clus	NOUN
m-392	101	14	-	-	PUNCT
m-392	101	15	tered	tere	VERB
m-392	101	16	binary	binary	ADJ
m-392	101	17	response	response	NOUN
m-392	101	18	models	model	NOUN
m-392	101	19	.	.	PUNCT
m-392	102	1	libyan	libyan	ADJ
m-392	102	2	journal	journal	PROPN
m-392	102	3	of	of	ADP
m-392	102	4	science	science	PROPN
m-392	102	5	&	&	CCONJ
m-392	102	6	technology	technology	PROPN
m-392	102	7	;	;	PUNCT
m-392	102	8	vol	vol	NOUN
m-392	102	9	.	.	PUNCT
m-392	103	1	(	(	PUNCT
m-392	103	2	10:1	10:1	NUM
m-392	103	3	)	)	PUNCT
m-392	103	4	20	20	NUM
m-392	103	5	-	-	SYM
m-392	103	6	22	22	NUM
m-392	103	7	.	.	PUNCT
m-392	104	1	el	el	PROPN
m-392	104	2	-	-	PUNCT
m-392	104	3	saeiti	saeiti	PROPN
m-392	104	4	,	,	PUNCT
m-392	104	5	i.	i.	PROPN
m-392	104	6	n.(2013	n.(2013	PROPN
m-392	104	7	):	):	PUNCT
m-392	104	8	“	"	PUNCT
m-392	104	9	adjusted	adjusted	ADJ
m-392	104	10	variance	variance	NOUN
m-392	104	11	components	component	NOUN
m-392	104	12	for	for	ADP
m-392	104	13	unbalanced	unbalanced	ADJ
m-392	104	14	clustered	clustered	ADJ
m-392	104	15	binary	binary	ADJ
m-392	104	16	data	datum	NOUN
m-392	104	17	models	model	NOUN
m-392	104	18	”	"	PUNCT
m-392	104	19	.	.	PUNCT
m-392	105	1	ph	ph	PROPN
m-392	105	2	.	.	PROPN
m-392	105	3	doctoral	doctoral	ADJ
m-392	105	4	“	"	PUNCT
m-392	105	5	university	university	NOUN
m-392	105	6	of	of	ADP
m-392	105	7	northern	northern	ADJ
m-392	105	8	colorado	colorado	PROPN
m-392	105	9	.	.	PUNCT
m-392	105	10	”	"	PUNCT
m-392	106	1	gning	gning	NOUN
m-392	106	2	,	,	PUNCT
m-392	106	3	l.(2013	l.(2013	PROPN
m-392	106	4	)	)	PUNCT
m-392	106	5	.	.	PUNCT
m-392	107	1	on	on	ADP
m-392	107	2	the	the	DET
m-392	107	3	existence	existence	NOUN
m-392	107	4	of	of	ADP
m-392	107	5	maximum	maximum	ADJ
m-392	107	6	likelihood	likelihood	NOUN
m-392	107	7	estimators	estimator	NOUN
m-392	107	8	in	in	ADP
m-392	107	9	poissongamma	poissongamma	PROPN
m-392	107	10	hglm	hglm	NOUN
m-392	107	11	and	and	CCONJ
m-392	107	12	negative	negative	ADJ
m-392	107	13	binomial	binomial	ADJ
m-392	107	14	regression	regression	NOUN
m-392	107	15	model	model	NOUN
m-392	107	16	.	.	PUNCT
m-392	108	1	electronic	electronic	ADJ
m-392	108	2	journal	journal	NOUN
m-392	108	3	of	of	ADP
m-392	108	4	statistics	statistic	NOUN
m-392	108	5	;	;	PUNCT
m-392	108	6	vol	vol	NOUN
m-392	108	7	.	.	PUNCT
m-392	109	1	(	(	PUNCT
m-392	109	2	7	7	NUM
m-392	109	3	)	)	PUNCT
m-392	109	4	,	,	PUNCT
m-392	109	5	2577–2594	2577–2594	NUM
m-392	109	6	heo	heo	PROPN
m-392	109	7	,	,	PUNCT
m-392	109	8	m.	m.	NOUN
m-392	109	9	and	and	CCONJ
m-392	109	10	leon	leon	PROPN
m-392	109	11	,	,	PUNCT
m-392	109	12	a.	a.	NOUN
m-392	109	13	(	(	PUNCT
m-392	109	14	2005	2005	NUM
m-392	109	15	)	)	PUNCT
m-392	109	16	.	.	PUNCT
m-392	110	1	performance	performance	NOUN
m-392	110	2	of	of	ADP
m-392	110	3	a	a	DET
m-392	110	4	mixed	mixed	ADJ
m-392	110	5	effects	effect	NOUN
m-392	110	6	logistic	logistic	ADJ
m-392	110	7	regression	regression	NOUN
m-392	110	8	model	model	NOUN
m-392	110	9	for	for	ADP
m-392	110	10	binary	binary	ADJ
m-392	110	11	outcomes	outcome	NOUN
m-392	110	12	with	with	ADP
m-392	110	13	unequal	unequal	ADJ
m-392	110	14	cluster	cluster	NOUN
m-392	110	15	size	size	NOUN
m-392	110	16	.	.	PUNCT
m-392	111	1	biopharmaceutical	biopharmaceutical	PROPN
m-392	111	2	statistics,15:513	statistics,15:513	PROPN
m-392	111	3	-	-	PUNCT
m-392	111	4	526	526	NUM
m-392	111	5	.	.	PUNCT
m-392	112	1	l.gning	l.gning	NOUN
m-392	112	2	and	and	CCONJ
m-392	112	3	d.	d.	PROPN
m-392	112	4	pierre	pierre	PROPN
m-392	112	5	-	-	PUNCT
m-392	112	6	loti	loti	PROPN
m-392	112	7	-	-	PUNCT
m-392	112	8	viaud	viaud	NOUN
m-392	112	9	.	.	PUNCT
m-392	113	1	(	(	PUNCT
m-392	113	2	2012	2012	NUM
m-392	113	3	):	):	PUNCT
m-392	113	4	on	on	ADP
m-392	113	5	the	the	DET
m-392	113	6	existence	existence	NOUN
m-392	113	7	of	of	ADP
m-392	113	8	maximum	maximum	ADJ
m-392	113	9	likelihood	likelihood	NOUN
m-392	113	10	estimators	estimator	NOUN
m-392	113	11	in	in	ADP
m-392	113	12	poisson	poisson	PROPN
m-392	113	13	-	-	PUNCT
m-392	113	14	gamma	gamma	NOUN
m-392	113	15	hglm	hglm	NOUN
m-392	113	16	and	and	CCONJ
m-392	113	17	negative	negative	ADJ
m-392	113	18	binomial	binomial	ADJ
m-392	113	19	regression	regression	NOUN
m-392	113	20	model	model	NOUN
m-392	113	21	.	.	PUNCT
m-392	114	1	lalonde	lalonde	PROPN
m-392	114	2	,	,	PUNCT
m-392	114	3	t.	t.	PROPN
m-392	114	4	l.	l.	PROPN
m-392	114	5	(	(	PUNCT
m-392	114	6	2009	2009	NUM
m-392	114	7	)	)	PUNCT
m-392	114	8	.	.	PUNCT
m-392	115	1	components	component	NOUN
m-392	115	2	of	of	ADP
m-392	115	3	overdispersion	overdispersion	NOUN
m-392	115	4	in	in	ADP
m-392	115	5	hierarchical	hierarchical	ADJ
m-392	115	6	generalized	generalized	ADJ
m-392	115	7	linear	linear	ADJ
m-392	115	8	models	model	NOUN
m-392	115	9	.	.	PUNCT
m-392	116	1	dissertations	dissertation	NOUN
m-392	116	2	”	"	PUNCT
m-392	116	3	university	university	NOUN
m-392	116	4	of	of	ADP
m-392	116	5	northern	northern	ADJ
m-392	116	6	colorado	colorado	PROPN
m-392	116	7	”	"	PUNCT
m-392	116	8	.	.	PUNCT
m-392	117	1	lee	lee	PROPN
m-392	117	2	,	,	PUNCT
m-392	117	3	y.	y.	PROPN
m-392	117	4	,	,	PUNCT
m-392	117	5	&	&	CCONJ
m-392	117	6	nelder	nelder	PROPN
m-392	117	7	,	,	PUNCT
m-392	117	8	j.	j.	PROPN
m-392	117	9	a.	a.	PROPN
m-392	117	10	(	(	PUNCT
m-392	117	11	2006	2006	NUM
m-392	117	12	)	)	PUNCT
m-392	117	13	.	.	PUNCT
m-392	118	1	double	double	ADJ
m-392	118	2	hierarchical	hierarchical	ADJ
m-392	118	3	generalized	generalized	ADJ
m-392	118	4	linear	linear	ADJ
m-392	118	5	models	model	NOUN
m-392	118	6	.	.	PUNCT
m-392	119	1	journal	journal	NOUN
m-392	119	2	of	of	ADP
m-392	119	3	the	the	DET
m-392	119	4	royal	royal	ADJ
m-392	119	5	statistical	statistical	ADJ
m-392	119	6	society	society	NOUN
m-392	119	7	,	,	PUNCT
m-392	119	8	series	series	NOUN
m-392	119	9	b	b	PROPN
m-392	119	10	(	(	PUNCT
m-392	119	11	methodological	methodological	ADJ
m-392	119	12	)	)	PUNCT
m-392	119	13	,	,	PUNCT
m-392	119	14	55	55	NUM
m-392	119	15	,	,	PUNCT
m-392	119	16	139	139	NUM
m-392	119	17	-	-	SYM
m-392	119	18	185	185	NUM
m-392	119	19	.	.	PUNCT
m-392	120	1	lee	lee	PROPN
m-392	120	2	,	,	PUNCT
m-392	120	3	y.	y.	PROPN
m-392	120	4	and	and	CCONJ
m-392	120	5	nelder	nelder	PROPN
m-392	120	6	,	,	PUNCT
m-392	120	7	j.(1996	j.(1996	PROPN
m-392	120	8	):	):	PUNCT
m-392	120	9	hierarchical	hierarchical	ADJ
m-392	120	10	generalized	generalized	ADJ
m-392	120	11	linear	linear	NOUN
m-392	120	12	models	model	NOUN
m-392	120	13	journal	journal	NOUN
m-392	120	14	of	of	ADP
m-392	120	15	the	the	DET
m-392	120	16	royal	royal	ADJ
m-392	120	17	statistical	statistical	ADJ
m-392	120	18	society	society	NOUN
m-392	120	19	,	,	PUNCT
m-392	120	20	series	series	NOUN
m-392	120	21	b	b	PROPN
m-392	120	22	(	(	PUNCT
m-392	120	23	methodological	methodological	ADJ
m-392	120	24	)	)	PUNCT
m-392	120	25	,	,	PUNCT
m-392	120	26	58	58	NUM
m-392	120	27	(	(	PUNCT
m-392	120	28	4	4	NUM
m-392	120	29	)	)	PUNCT
m-392	120	30	,	,	PUNCT
m-392	120	31	619	619	NUM
m-392	120	32	-	-	SYM
m-392	120	33	678	678	NUM
m-392	120	34	.	.	PUNCT
m-392	121	1	maria	maria	PROPN
m-392	121	2	,	,	PUNCT
m-392	121	3	a.(1997	a.(1997	ADV
m-392	121	4	):	):	PUNCT
m-392	121	5	"	"	PUNCT
m-392	121	6	introduction	introduction	NOUN
m-392	121	7	to	to	ADP
m-392	121	8	modeling	modeling	NOUN
m-392	121	9	and	and	CCONJ
m-392	121	10	simulation	simulation	NOUN
m-392	121	11	"	"	PUNCT
m-392	121	12	,	,	PUNCT
m-392	121	13	proceedings	proceeding	NOUN
m-392	121	14	of	of	ADP
m-392	121	15	the	the	DET
m-392	121	16	1997	1997	NUM
m-392	121	17	winter	winter	NOUN
m-392	121	18	simulation	simulation	NOUN
m-392	121	19	conference	conference	PROPN
m-392	121	20	.	.	PUNCT
m-392	122	1	mcculloch	mcculloch	PROPN
m-392	122	2	,	,	PUNCT
m-392	122	3	c.e	c.e	PROPN
m-392	122	4	.	.	PROPN
m-392	122	5	,	,	PUNCT
m-392	122	6	and	and	CCONJ
m-392	122	7	shayle	shayle	NOUN
m-392	122	8	,	,	PUNCT
m-392	122	9	r.s.(2001	r.s.(2001	PROPN
m-392	122	10	):	):	PUNCT
m-392	122	11	generalized	generalize	VERB
m-392	122	12	,	,	PUNCT
m-392	122	13	linear	linear	ADJ
m-392	122	14	,	,	PUNCT
m-392	122	15	and	and	CCONJ
m-392	122	16	mixed	mixed	ADJ
m-392	122	17	models	model	NOUN
m-392	122	18	.	.	PUNCT
m-392	123	1	ny	ny	PROPN
m-392	123	2	:	:	PUNCT
m-392	123	3	john	john	PROPN
m-392	123	4	wiley	wiley	PROPN
m-392	123	5	&	&	CCONJ
m-392	123	6	sons	sons	PROPN
m-392	123	7	,	,	PUNCT
m-392	123	8	inc	inc	PROPN
m-392	123	9	.	.	PROPN
m-392	123	10	rönnegård	rönnegård	PROPN
m-392	123	11	,	,	PUNCT
m-392	123	12	l.	l.	PROPN
m-392	123	13	,	,	PUNCT
m-392	123	14	alam	alam	PROPN
m-392	123	15	,	,	PUNCT
m-392	123	16	m.	m.	NOUN
m-392	123	17	and	and	CCONJ
m-392	123	18	shen	shen	PROPN
m-392	123	19	,	,	PUNCT
m-392	123	20	x.	x.	PROPN
m-392	123	21	(	(	PUNCT
m-392	123	22	2010	2010	NUM
m-392	123	23	)	)	PUNCT
m-392	123	24	hglm	hglm	NOUN
m-392	123	25	package	package	NOUN
m-392	123	26	(	(	PUNCT
m-392	123	27	version	version	NOUN
m-392	123	28	2.0	2.0	NUM
m-392	123	29	)	)	PUNCT
m-392	123	30	package	package	NOUN
m-392	123	31	maintainer	maintainer	PROPN
m-392	123	32	ijo	ijo	PROPN
m-392	123	33	international	international	PROPN
m-392	123	34	journal	journal	PROPN
m-392	123	35	of	of	ADP
m-392	123	36	mathematics	mathematics	PROPN
m-392	123	37	volume	volume	PROPN
m-392	123	38	3|	3|	NUM
m-392	123	39	issue	issue	NOUN
m-392	124	1	12|	12|	NUM
m-392	124	2	december	december	PROPN
m-392	124	3	|	|	NOUN
m-392	124	4	2020	2020	NUM
m-392	124	5	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	124	6	26	26	NUM
m-392	124	7	http://projecteuclid.org/ejs	http://projecteuclid.org/ejs	ADV
m-392	124	8	http://projecteuclid.org/ejs	http://projecteuclid.org/ejs	ADV
m-392	124	9	appendix	appendix	VERB
m-392	124	10	mydata	mydata	ADJ
m-392	124	11	=	=	NOUN
m-392	124	12	function(seed	function(seed	PROPN
m-392	124	13	)	)	PUNCT
m-392	124	14	{	{	PUNCT
m-392	124	15	set.seed(seed	set.seed(seed	NOUN
m-392	124	16	)	)	PUNCT
m-392	124	17	beta0	beta0	NOUN
m-392	125	1	=	=	SYM
m-392	125	2	1	1	NUM
m-392	125	3	beta1	beta1	NOUN
m-392	125	4	=	=	NOUN
m-392	125	5	0.2	0.2	NUM
m-392	125	6	beta2	beta2	NOUN
m-392	125	7	=	=	SYM
m-392	125	8	3.1	3.1	NUM
m-392	125	9	#	#	SYM
m-392	125	10	#	#	SYM
m-392	125	11	#	#	SYM
m-392	125	12	#	#	SYM
m-392	125	13	#	#	SYM
m-392	125	14	#	#	SYM
m-392	125	15	#	#	SYM
m-392	125	16	#	#	SYM
m-392	125	17	#	#	SYM
m-392	125	18	#	#	NOUN
m-392	125	19	for	for	ADP
m-392	125	20	poi	poi	PROPN
m-392	125	21	-	-	PUNCT
m-392	125	22	gam	gam	NOUN
m-392	125	23	#	#	SYM
m-392	125	24	#	#	SYM
m-392	125	25	#	#	NOUN
m-392	125	26	n.clus	n.clus	ADP
m-392	125	27	<	<	NOUN
m-392	125	28	2	2	NUM
m-392	125	29	#	#	NOUN
m-392	125	30	no	no	NOUN
m-392	125	31	.	.	PUNCT
m-392	125	32	of	of	ADP
m-392	125	33	clusters	cluster	NOUN
m-392	125	34	n.per.clus	n.per.clus	ADV
m-392	125	35	<	<	X
m-392	125	36	5	5	NUM
m-392	125	37	#	#	SYM
m-392	125	38	no	no	NOUN
m-392	125	39	.	.	PUNCT
m-392	125	40	of	of	ADP
m-392	125	41	obs	obs	PROPN
m-392	125	42	.	.	PUNCT
m-392	126	1	per	per	ADP
m-392	126	2	cluster	cluster	NOUN
m-392	126	3	for	for	ADP
m-392	126	4	equal	equal	ADJ
m-392	126	5	sigma2_u	sigma2_u	NOUN
m-392	126	6	<	<	NOUN
m-392	126	7	0.2	0.2	NUM
m-392	126	8	#	#	NUM
m-392	126	9	variance	variance	NOUN
m-392	126	10	of	of	ADP
m-392	126	11	random	random	ADJ
m-392	126	12	effect	effect	NOUN
m-392	126	13	sigma2_e	sigma2_e	NOUN
m-392	126	14	<	<	NOUN
m-392	126	15	1	1	NUM
m-392	126	16	#	#	NUM
m-392	126	17	residual	residual	ADJ
m-392	126	18	variance	variance	NOUN
m-392	126	19	sigma1<2	sigma1<2	PROPN
m-392	126	20	nn	nn	X
m-392	126	21	<	<	X
m-392	126	22	n.clus*n.per.clus	n.clus*n.per.clus	ADJ
m-392	126	23	beta	beta	NOUN
m-392	126	24	=	=	SYM
m-392	126	25	matrix(c(beta0,beta1,beta2),3,1	matrix(c(beta0,beta1,beta2),3,1	PROPN
m-392	126	26	)	)	PUNCT
m-392	126	27	y	y	PROPN
m-392	126	28	=	=	PROPN
m-392	126	29	matrix(0,nn,1	matrix(0,nn,1	NOUN
m-392	126	30	)	)	PUNCT
m-392	126	31	x	x	X
m-392	126	32	=	=	NOUN
m-392	126	33	matrix(c(rep(1,nn),rep(0,nn),rep(0,nn)),nn,3	matrix(c(rep(1,nn),rep(0,nn),rep(0,nn)),nn,3	NUM
m-392	126	34	)	)	PUNCT
m-392	126	35	z	z	NOUN
m-392	126	36	=	=	NOUN
m-392	126	37	matrix(0,nn	matrix(0,nn	NOUN
m-392	126	38	,	,	PUNCT
m-392	126	39	n.clus	n.clus	ADV
m-392	126	40	)	)	PUNCT
m-392	126	41	a	a	DET
m-392	126	42	<	<	X
m-392	126	43	rnorm(n.clus	rnorm(n.clus	ADJ
m-392	126	44	,	,	PUNCT
m-392	126	45	0	0	NUM
m-392	126	46	,	,	PUNCT
m-392	126	47	sqrt(sigma2_u	sqrt(sigma2_u	NOUN
m-392	126	48	)	)	PUNCT
m-392	126	49	)	)	PUNCT
m-392	127	1	e	e	X
m-392	127	2	<	<	X
m-392	127	3	rnorm(nn	rnorm(nn	PROPN
m-392	127	4	,	,	PUNCT
m-392	127	5	0	0	NUM
m-392	127	6	,	,	PUNCT
m-392	127	7	sqrt(sigma2_e	sqrt(sigma2_e	NOUN
m-392	127	8	)	)	PUNCT
m-392	127	9	)	)	PUNCT
m-392	127	10	#	#	SYM
m-392	127	11	#	#	NOUN
m-392	127	12	generate	generate	NOUN
m-392	127	13	x	x	NOUN
m-392	127	14	-	-	NOUN
m-392	127	15	values	value	NOUN
m-392	127	16	from	from	ADP
m-392	127	17	normal	normal	ADJ
m-392	127	18	distirbuation	distirbuation	NOUN
m-392	127	19	#	#	NOUN
m-392	127	20	#	#	NOUN
m-392	127	21	x[,2	x[,2	X
m-392	127	22	]	]	X
m-392	127	23	=	=	SYM
m-392	127	24	rnorm(nn,3,sigma1	rnorm(nn,3,sigma1	X
m-392	127	25	)	)	PUNCT
m-392	127	26	x[,3]=rpois(nn,3	x[,3]=rpois(nn,3	PROPN
m-392	127	27	)	)	PUNCT
m-392	127	28	x_d	x_d	PUNCT
m-392	128	1	<	<	X
m-392	128	2	matrix(c(rep(1,nn),rep(0,nn),rep(0,nn)),nn,3	matrix(c(rep(1,nn),rep(0,nn),rep(0,nn)),nn,3	NUM
m-392	128	3	)	)	PUNCT
m-392	128	4	z	z	NOUN
m-392	128	5	<	<	X
m-392	128	6	diag(n.clus)%x%rep(1	diag(n.clus)%x%rep(1	NOUN
m-392	128	7	,	,	PUNCT
m-392	128	8	n.per.clus	n.per.clus	ADJ
m-392	128	9	)	)	PUNCT
m-392	128	10	u	u	NOUN
m-392	128	11	<	<	X
m-392	128	12	rgamma(n.clus,1	rgamma(n.clus,1	X
m-392	128	13	)	)	PUNCT
m-392	128	14	eta	eta	PROPN
m-392	128	15	<	<	X
m-392	128	16	exp(beta0+beta1*x[,2]+z%*%u	exp(beta0+beta1*x[,2]+z%*%u	NOUN
m-392	128	17	)	)	PUNCT
m-392	128	18	y	y	PROPN
m-392	128	19	<	<	X
m-392	128	20	rpois(length(eta	rpois(length(eta	PROPN
m-392	128	21	)	)	PUNCT
m-392	128	22	,	,	PUNCT
m-392	128	23	eta	eta	PROPN
m-392	128	24	)	)	PUNCT
m-392	128	25	list	list	NOUN
m-392	128	26	(	(	PUNCT
m-392	128	27	x	x	X
m-392	128	28	=	=	SYM
m-392	128	29	x	x	X
m-392	128	30	,	,	PUNCT
m-392	128	31	y	y	PROPN
m-392	128	32	=	=	PROPN
m-392	128	33	y	y	PROPN
m-392	128	34	,	,	PUNCT
m-392	128	35	u	u	NOUN
m-392	128	36	=	=	NOUN
m-392	128	37	u	u	NOUN
m-392	128	38	,	,	PUNCT
m-392	128	39	z	z	PROPN
m-392	128	40	=	=	PROPN
m-392	128	41	z	z	PROPN
m-392	128	42	,	,	PUNCT
m-392	128	43	x_d	x_d	PUNCT
m-392	128	44	=	=	SYM
m-392	128	45	x_d	x_d	NOUN
m-392	128	46	)	)	PUNCT
m-392	128	47	}	}	PUNCT
m-392	128	48	#	#	SYM
m-392	128	49	#	#	SYM
m-392	128	50	#	#	SYM
m-392	128	51	#	#	SYM
m-392	128	52	#	#	SYM
m-392	128	53	#	#	SYM
m-392	128	54	#	#	SYM
m-392	128	55	#	#	SYM
m-392	128	56	#	#	SYM
m-392	128	57	#	#	SYM
m-392	128	58	#	#	SYM
m-392	128	59	#	#	SYM
m-392	128	60	#	#	SYM
m-392	128	61	#	#	SYM
m-392	128	62	#	#	SYM
m-392	128	63	#	#	SYM
m-392	128	64	#	#	SYM
m-392	128	65	#	#	SYM
m-392	128	66	#	#	SYM
m-392	128	67	#	#	SYM
m-392	128	68	#	#	SYM
m-392	128	69	#	#	SYM
m-392	128	70	#	#	SYM
m-392	128	71	#	#	SYM
m-392	128	72	#	#	SYM
m-392	128	73	#	#	SYM
m-392	128	74	#	#	SYM
m-392	128	75	#	#	SYM
m-392	128	76	#	#	SYM
m-392	128	77	#	#	SYM
m-392	128	78	#	#	SYM
m-392	128	79	#	#	SYM
m-392	128	80	#	#	SYM
m-392	128	81	#	#	SYM
m-392	128	82	#	#	SYM
m-392	128	83	#	#	SYM
m-392	128	84	#	#	SYM
m-392	128	85	#	#	SYM
m-392	128	86	#	#	SYM
m-392	128	87	#	#	SYM
m-392	128	88	#	#	SYM
m-392	128	89	#	#	SYM
m-392	128	90	#	#	NOUN
m-392	128	91	#	#	NOUN
m-392	128	92	power	power	NOUN
m-392	128	93	for	for	ADP
m-392	128	94	h	h	NOUN
m-392	128	95	-	-	PUNCT
m-392	128	96	likelihood	likelihood	NOUN
m-392	128	97	function	function	NOUN
m-392	128	98	#	#	NOUN
m-392	128	99	#	#	NOUN
m-392	128	100	by	by	ADP
m-392	128	101	using	use	VERB
m-392	128	102	hglm	hglm	NOUN
m-392	128	103	function	function	NOUN
m-392	128	104	#	#	NOUN
m-392	128	105	#	#	SYM
m-392	128	106	#	#	SYM
m-392	128	107	#	#	SYM
m-392	128	108	#	#	SYM
m-392	128	109	#	#	SYM
m-392	128	110	#	#	SYM
m-392	128	111	#	#	SYM
m-392	128	112	#	#	SYM
m-392	128	113	=	=	NOUN
m-392	128	114	=	=	SYM
m-392	128	115	=	=	SYM
m-392	128	116	=	=	SYM
m-392	128	117	=	=	SYM
m-392	128	118	=	=	SYM
m-392	128	119	=	=	SYM
m-392	128	120	=	=	SYM
m-392	128	121	=	=	SYM
m-392	128	122	=	=	SYM
m-392	128	123	=	=	SYM
m-392	128	124	=	=	SYM
m-392	128	125	=	=	SYM
m-392	128	126	=	=	SYM
m-392	128	127	=	=	SYM
m-392	128	128	=	=	SYM
m-392	128	129	=	=	SYM
m-392	128	130	=	=	SYM
m-392	128	131	=	=	SYM
m-392	128	132	=	=	SYM
m-392	128	133	=	=	SYM
m-392	128	134	=	=	SYM
m-392	128	135	=	=	SYM
m-392	128	136	=	=	SYM
m-392	128	137	=	=	SYM
m-392	128	138	=	=	SYM
m-392	128	139	=	=	SYM
m-392	128	140	=	=	SYM
m-392	128	141	=	=	SYM
m-392	128	142	=	=	SYM
m-392	128	143	=	=	NOUN
m-392	128	144	=	=	NOUN
m-392	128	145	#	#	NOUN
m-392	128	146	library(mass	library(mass	NOUN
m-392	128	147	)	)	PUNCT
m-392	128	148	library(hglm	library(hglm	PROPN
m-392	128	149	)	)	PUNCT
m-392	128	150	sima=	sima=	NOUN
m-392	128	151	function	function	NOUN
m-392	128	152	(	(	PUNCT
m-392	128	153	n1	n1	NOUN
m-392	128	154	)	)	PUNCT
m-392	128	155	{	{	PUNCT
m-392	128	156	set.seed(1234	set.seed(1234	X
m-392	128	157	)	)	PUNCT
m-392	128	158	alpha	alpha	NOUN
m-392	128	159	<	<	X
m-392	128	160	0.05	0.05	NUM
m-392	128	161	b21count	b21count	PROPN
m-392	128	162	<	<	X
m-392	128	163	0	0	NUM
m-392	128	164	b22count	b22count	NOUN
m-392	128	165	<	<	X
m-392	128	166	0	0	NUM
m-392	128	167	s.e2	s.e2	NOUN
m-392	128	168	<	<	X
m-392	128	169	matrix(0,nrow	matrix(0,nrow	PROPN
m-392	128	170	=	=	SYM
m-392	128	171	n1	n1	NOUN
m-392	128	172	,	,	PUNCT
m-392	128	173	ncol=1	ncol=1	PROPN
m-392	128	174	)	)	PUNCT
m-392	128	175	b.e21	b.e21	NUM
m-392	128	176	<	<	X
m-392	128	177	matrix(0,nrow	matrix(0,nrow	PROPN
m-392	128	178	=	=	SYM
m-392	128	179	n1	n1	NOUN
m-392	128	180	,	,	PUNCT
m-392	128	181	ncol=1	ncol=1	PROPN
m-392	128	182	)	)	PUNCT
m-392	128	183	b.e22	b.e22	X
m-392	128	184	<	<	X
m-392	128	185	matrix(0,nrow	matrix(0,nrow	PROPN
m-392	128	186	=	=	SYM
m-392	128	187	n1	n1	NOUN
m-392	128	188	,	,	PUNCT
m-392	128	189	ncol=1	ncol=1	NUM
m-392	128	190	)	)	PUNCT
m-392	128	191	seeds	seed	NOUN
m-392	128	192	=	=	SYM
m-392	128	193	rnorm(n1,0,50	rnorm(n1,0,50	NOUN
m-392	128	194	)	)	PUNCT
m-392	128	195	set.seed(seeds	set.seed(seed	NOUN
m-392	128	196	)	)	PUNCT
m-392	128	197	for(i	for(i	NOUN
m-392	128	198	in	in	ADP
m-392	128	199	1	1	NUM
m-392	128	200	:	:	SYM
m-392	128	201	n1	n1	NOUN
m-392	128	202	)	)	PUNCT
m-392	128	203	{	{	PUNCT
m-392	128	204	datta=	datta=	NUM
m-392	128	205	mydata(seeds[i	mydata(seeds[i	NOUN
m-392	128	206	]	]	PUNCT
m-392	128	207	)	)	PUNCT
m-392	128	208	x	x	X
m-392	129	1	=	=	X
m-392	129	2	datta$x	datta$x	NOUN
m-392	129	3	y	y	PROPN
m-392	129	4	=	=	PROPN
m-392	129	5	datta$y	datta$y	PROPN
m-392	129	6	x_d	x_d	NOUN
m-392	129	7	=	=	SYM
m-392	129	8	datta$x_d	datta$x_d	X
m-392	129	9	z	z	PROPN
m-392	129	10	=	=	PRON
m-392	129	11	datta$z	datta$z	NOUN
m-392	129	12	#	#	NOUN
m-392	129	13	=	=	NOUN
m-392	129	14	=	=	SYM
m-392	129	15	=	=	SYM
m-392	129	16	=	=	SYM
m-392	129	17	=	=	SYM
m-392	129	18	=	=	SYM
m-392	129	19	=	=	SYM
m-392	129	20	=	=	SYM
m-392	129	21	=	=	SYM
m-392	129	22	=	=	SYM
m-392	129	23	=	=	SYM
m-392	129	24	=	=	SYM
m-392	129	25	=	=	SYM
m-392	129	26	=	=	SYM
m-392	129	27	=	=	SYM
m-392	129	28	=	=	SYM
m-392	129	29	=	=	SYM
m-392	129	30	=	=	SYM
m-392	129	31	=	=	SYM
m-392	129	32	=	=	SYM
m-392	129	33	=	=	SYM
m-392	129	34	=	=	SYM
m-392	129	35	=	=	SYM
m-392	129	36	=	=	ADJ
m-392	129	37	h	h	ADJ
m-392	129	38	-	-	PUNCT
m-392	129	39	likelihood	likelihood	NOUN
m-392	129	40	method	method	NOUN
m-392	129	41	=	=	NOUN
m-392	129	42	=	=	SYM
m-392	129	43	=	=	SYM
m-392	129	44	=	=	SYM
m-392	129	45	=	=	SYM
m-392	129	46	=	=	SYM
m-392	129	47	=	=	SYM
m-392	129	48	=	=	SYM
m-392	129	49	=	=	SYM
m-392	129	50	=	=	SYM
m-392	129	51	=	=	SYM
m-392	129	52	=	=	SYM
m-392	129	53	=	=	SYM
m-392	129	54	=	=	SYM
m-392	129	55	=	=	SYM
m-392	129	56	=	=	SYM
m-392	129	57	=	=	SYM
m-392	129	58	=	=	SYM
m-392	129	59	=	=	SYM
m-392	129	60	=	=	SYM
m-392	129	61	=	=	SYM
m-392	129	62	=	=	SYM
m-392	129	63	=	=	SYM
m-392	129	64	=	=	SYM
m-392	129	65	=	=	SYM
m-392	129	66	=	=	NOUN
m-392	129	67	#	#	NOUN
m-392	129	68	r	r	NOUN
m-392	129	69	<	<	X
m-392	129	70	gamma.pois	gamma.pois	X
m-392	129	71	<	<	X
m-392	129	72	hglm(y	hglm(y	PROPN
m-392	129	73	=	=	PROPN
m-392	129	74	y	y	PROPN
m-392	129	75	,	,	PUNCT
m-392	129	76	x	x	X
m-392	129	77	=	=	SYM
m-392	129	78	x	x	X
m-392	129	79	,	,	PUNCT
m-392	129	80	z	z	PROPN
m-392	129	81	=	=	SYM
m-392	129	82	z	z	NOUN
m-392	129	83	,	,	PUNCT
m-392	129	84	x.disp	x.disp	PROPN
m-392	129	85	=	=	SYM
m-392	129	86	x_d	x_d	PROPN
m-392	129	87	,	,	PUNCT
m-392	129	88	family	family	NOUN
m-392	129	89	=	=	SYM
m-392	129	90	poisson(link	poisson(link	NOUN
m-392	129	91	=	=	SYM
m-392	129	92	log	log	PROPN
m-392	129	93	)	)	PUNCT
m-392	129	94	,	,	PUNCT
m-392	129	95	rand.family	rand.family	ADV
m-392	129	96	=	=	SYM
m-392	129	97	gamma(link	gamma(link	NOUN
m-392	129	98	=	=	SYM
m-392	129	99	log	log	PROPN
m-392	129	100	)	)	PUNCT
m-392	129	101	)	)	PUNCT
m-392	129	102	ijo	ijo	PROPN
m-392	129	103	international	international	PROPN
m-392	129	104	journal	journal	PROPN
m-392	129	105	of	of	ADP
m-392	129	106	mathematics	mathematics	PROPN
m-392	129	107	volume	volume	PROPN
m-392	129	108	3|	3|	NUM
m-392	129	109	issue	issue	NOUN
m-392	129	110	12|	12|	NUM
m-392	129	111	december	december	PROPN
m-392	129	112	|	|	NOUN
m-392	129	113	2020	2020	NUM
m-392	129	114	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	129	115	27	27	NUM
m-392	129	116	ss=	ss=	PROPN
m-392	129	117	summary(r	summary(r	PROPN
m-392	129	118	)	)	PUNCT
m-392	129	119	betas	beta	NOUN
m-392	129	120	<	<	X
m-392	129	121	r$fixef	r$fixef	PROPN
m-392	129	122	se	se	X
m-392	129	123	<	<	X
m-392	129	124	r$sefe	r$sefe	PROPN
m-392	129	125	zval	zval	NOUN
m-392	129	126	<	<	X
m-392	129	127	betas	betas	PROPN
m-392	129	128	/	/	SYM
m-392	129	129	se	se	X
m-392	129	130	pval	pval	NOUN
m-392	129	131	<	<	X
m-392	129	132	2	2	NUM
m-392	129	133	*	*	SYM
m-392	129	134	pnorm(abs(zval	pnorm(abs(zval	NOUN
m-392	129	135	)	)	PUNCT
m-392	129	136	,	,	PUNCT
m-392	129	137	lower.tail	lower.tail	NOUN
m-392	129	138	=	=	SYM
m-392	129	139	false	false	ADJ
m-392	129	140	)	)	PUNCT
m-392	129	141	s.e2[i	s.e2[i	NOUN
m-392	129	142	,	,	PUNCT
m-392	129	143	]	]	PUNCT
m-392	129	144	<	<	X
m-392	129	145	se[2	se[2	PROPN
m-392	129	146	]	]	PUNCT
m-392	129	147	b.e21[i	b.e21[i	NOUN
m-392	129	148	,	,	PUNCT
m-392	129	149	]	]	PUNCT
m-392	129	150	<	<	X
m-392	129	151	betas[2	betas[2	PROPN
m-392	129	152	]	]	X
m-392	129	153	b.e22[i	b.e22[i	PROPN
m-392	129	154	,	,	PUNCT
m-392	129	155	]	]	PUNCT
m-392	129	156	<	<	X
m-392	129	157	betas[3	betas[3	X
m-392	129	158	]	]	X
m-392	129	159	p21	p21	NOUN
m-392	129	160	=	=	SYM
m-392	129	161	pval[2	pval[2	PROPN
m-392	129	162	]	]	X
m-392	129	163	if(p21	if(p21	NOUN
m-392	129	164	<	<	X
m-392	129	165	alpha){b21count	alpha){b21count	PROPN
m-392	129	166	=	=	SYM
m-392	129	167	b21count+1	b21count+1	PROPN
m-392	129	168	}	}	PUNCT
m-392	129	169	p22	p22	NOUN
m-392	129	170	=	=	SYM
m-392	129	171	pval[3	pval[3	PROPN
m-392	129	172	]	]	X
m-392	129	173	if	if	SCONJ
m-392	129	174	(	(	PUNCT
m-392	129	175	p22	p22	NOUN
m-392	129	176	<	<	X
m-392	129	177	alpha){b22count	alpha){b22count	NOUN
m-392	129	178	=	=	PUNCT
m-392	129	179	b22count+1	b22count+1	NOUN
m-392	129	180	}	}	PUNCT
m-392	129	181	}	}	PUNCT
m-392	129	182	typei2	typei2	NOUN
m-392	130	1	=	=	SYM
m-392	130	2	b22count	b22count	NOUN
m-392	130	3	/	/	SYM
m-392	130	4	n1	n1	NOUN
m-392	130	5	power2	power2	NOUN
m-392	130	6	=	=	SYM
m-392	130	7	b21count	b21count	PROPN
m-392	130	8	/	/	SYM
m-392	130	9	n1	n1	PROPN
m-392	130	10	se2	se2	PROPN
m-392	130	11	<	<	X
m-392	130	12	sum(s.e2)/n1	sum(s.e2)/n1	PROPN
m-392	130	13	be21	be21	PROPN
m-392	130	14	<	<	PART
m-392	131	1	sum(b.e21)/n1	sum(b.e21)/n1	PROPN
m-392	131	2	be22	be22	PROPN
m-392	131	3	<	<	PART
m-392	131	4	sum(b.e22)/n1	sum(b.e22)/n1	PROPN
m-392	131	5	list(ss	list(ss	PROPN
m-392	131	6	=	=	PROPN
m-392	131	7	ss	ss	PROPN
m-392	131	8	,	,	PUNCT
m-392	131	9	be21	be21	PROPN
m-392	131	10	=	=	SYM
m-392	131	11	be21,be22	be21,be22	NOUN
m-392	131	12	=	=	NOUN
m-392	131	13	be22,power2	be22,power2	NOUN
m-392	131	14	=	=	NOUN
m-392	131	15	power2,typei2	power2,typei2	NOUN
m-392	131	16	=	=	NOUN
m-392	131	17	typei2,se2	typei2,se2	NOUN
m-392	131	18	=	=	ADJ
m-392	131	19	se2	se2	NOUN
m-392	131	20	)	)	PUNCT
m-392	131	21	}	}	PUNCT
m-392	131	22	ijo	ijo	PROPN
m-392	131	23	international	international	PROPN
m-392	131	24	journal	journal	PROPN
m-392	131	25	of	of	ADP
m-392	131	26	mathematics	mathematics	PROPN
m-392	131	27	volume	volume	PROPN
m-392	131	28	3|	3|	NUM
m-392	131	29	issue	issue	NOUN
m-392	131	30	12|	12|	NUM
m-392	131	31	december	december	PROPN
m-392	131	32	|	|	NOUN
m-392	131	33	2020	2020	NUM
m-392	131	34	http://ijojournals.com/index.php/m	http://ijojournals.com/index.php/m	VERB
m-392	131	35	28	28	NUM