id	sid	tid	token	lemma	pos
cana-5959	1	1	communications	communication	NOUN
cana-5959	1	2	on	on	ADP
cana-5959	1	3	applied	apply	VERB
cana-5959	1	4	nonlinear	nonlinear	ADJ
cana-5959	1	5	analysis	analysis	NOUN
cana-5959	1	6	issn	issn	NOUN
cana-5959	1	7	:	:	PUNCT
cana-5959	1	8	1074	1074	NUM
cana-5959	1	9	-	-	PUNCT
cana-5959	1	10	133x	133x	NUM
cana-5959	1	11	vol	vol	VERB
cana-5959	1	12	32	32	NUM
cana-5959	1	13	no	no	NOUN
cana-5959	1	14	.	.	PUNCT
cana-5959	2	1	10s	10	NOUN
cana-5959	2	2	(	(	PUNCT
cana-5959	2	3	2025	2025	NUM
cana-5959	2	4	)	)	PUNCT
cana-5959	2	5	3191	3191	NUM
cana-5959	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	2	7	robust	robust	ADJ
cana-5959	2	8	modeling	modeling	NOUN
cana-5959	2	9	of	of	ADP
cana-5959	2	10	over	over	ADP
cana-5959	2	11	dispersed	disperse	VERB
cana-5959	2	12	count	count	NOUN
cana-5959	2	13	data	datum	NOUN
cana-5959	2	14	using	use	VERB
cana-5959	2	15	an	an	DET
cana-5959	2	16	outlier	outlier	NOUN
cana-5959	2	17	-	-	PUNCT
cana-5959	2	18	weighted	weight	VERB
cana-5959	2	19	poisson	poisson	NOUN
cana-5959	2	20	regression	regression	NOUN
cana-5959	2	21	approach	approach	NOUN
cana-5959	2	22	1abobaker	1abobaker	PROPN
cana-5959	2	23	mohamed	mohamed	PROPN
cana-5959	2	24	jaber	jaber	PROPN
cana-5959	2	25	,	,	PUNCT
cana-5959	2	26	2mohamed	2mohamed	NUM
cana-5959	2	27	amraja	amraja	PROPN
cana-5959	2	28	mohamed	mohamed	PROPN
cana-5959	2	29	,	,	PUNCT
cana-5959	2	30	3aissa	3aissa	NUM
cana-5959	2	31	omar	omar	PROPN
cana-5959	2	32	assrhani	assrhani	PROPN
cana-5959	2	33	,	,	PUNCT
cana-5959	2	34	4hanadi	4hanadi	PROPN
cana-5959	2	35	abdullah	abdullah	PROPN
cana-5959	2	36	amhimmid	amhimmid	PROPN
cana-5959	2	37	,	,	PUNCT
cana-5959	2	38	5kasem	5kasem	NUM
cana-5959	2	39	abdinibi	abdinibi	NOUN
cana-5959	2	40	farag	farag	NOUN
cana-5959	2	41	,	,	PUNCT
cana-5959	2	42	1assistant	1assistant	NUM
cana-5959	2	43	professor	professor	NOUN
cana-5959	2	44	of	of	ADP
cana-5959	2	45	time	time	NOUN
cana-5959	2	46	series	series	PROPN
cana-5959	2	47	,	,	PUNCT
cana-5959	2	48	department	department	PROPN
cana-5959	2	49	of	of	ADP
cana-5959	2	50	statistics	statistic	NOUN
cana-5959	2	51	,	,	PUNCT
cana-5959	2	52	faculty	faculty	NOUN
cana-5959	2	53	of	of	ADP
cana-5959	2	54	science	science	NOUN
cana-5959	2	55	,	,	PUNCT
cana-5959	2	56	benghazi	benghazi	NOUN
cana-5959	2	57	university	university	NOUN
cana-5959	2	58	,	,	PUNCT
cana-5959	2	59	benghazi	benghazi	NOUN
cana-5959	2	60	,	,	PUNCT
cana-5959	2	61	libya	libya	PROPN
cana-5959	2	62	,	,	PUNCT
cana-5959	2	63	abobaker.jaber@uob.edu.ly	abobaker.jaber@uob.edu.ly	CCONJ
cana-5959	2	64	2associate	2associate	NUM
cana-5959	2	65	professor	professor	NOUN
cana-5959	2	66	of	of	ADP
cana-5959	2	67	applied	applied	ADJ
cana-5959	2	68	statistics	statistic	NOUN
cana-5959	2	69	,	,	PUNCT
cana-5959	2	70	department	department	NOUN
cana-5959	2	71	of	of	ADP
cana-5959	2	72	statistics	statistic	NOUN
cana-5959	2	73	,	,	PUNCT
cana-5959	2	74	faculty	faculty	NOUN
cana-5959	2	75	of	of	ADP
cana-5959	2	76	science	science	NOUN
cana-5959	2	77	,	,	PUNCT
cana-5959	2	78	sebha	sebha	PROPN
cana-5959	2	79	university	university	PROPN
cana-5959	2	80	,	,	PUNCT
cana-5959	2	81	sebha	sebha	NOUN
cana-5959	2	82	,	,	PUNCT
cana-5959	2	83	libya	libya	PROPN
cana-5959	2	84	,	,	PUNCT
cana-5959	2	85	moh.mohamed@sebhau.edu.ly	moh.mohamed@sebhau.edu.ly	PROPN
cana-5959	2	86	3assistant	3assistant	NUM
cana-5959	2	87	professor	professor	NOUN
cana-5959	2	88	of	of	ADP
cana-5959	2	89	applied	applied	ADJ
cana-5959	2	90	statistics	statistic	NOUN
cana-5959	2	91	,	,	PUNCT
cana-5959	2	92	department	department	NOUN
cana-5959	2	93	of	of	ADP
cana-5959	2	94	statistics	statistic	NOUN
cana-5959	2	95	,	,	PUNCT
cana-5959	2	96	faculty	faculty	NOUN
cana-5959	2	97	of	of	ADP
cana-5959	2	98	science	science	NOUN
cana-5959	2	99	,	,	PUNCT
cana-5959	2	100	sebha	sebha	PROPN
cana-5959	2	101	university	university	PROPN
cana-5959	2	102	,	,	PUNCT
cana-5959	2	103	sebha	sebha	NOUN
cana-5959	2	104	,	,	PUNCT
cana-5959	2	105	libya	libya	PROPN
cana-5959	2	106	,	,	PUNCT
cana-5959	2	107	ais.assrhani@sebhau.edu.ly	ais.assrhani@sebhau.edu.ly	VERB
cana-5959	2	108	4assistsnt	4assistsnt	NUM
cana-5959	2	109	lecturer	lecturer	NOUN
cana-5959	2	110	of	of	ADP
cana-5959	2	111	time	time	NOUN
cana-5959	2	112	series	series	NOUN
cana-5959	2	113	,	,	PUNCT
cana-5959	2	114	major	major	ADJ
cana-5959	2	115	of	of	ADP
cana-5959	2	116	statistics	statistic	NOUN
cana-5959	2	117	,	,	PUNCT
cana-5959	2	118	mathematics	mathematics	PROPN
cana-5959	2	119	department	department	PROPN
cana-5959	2	120	faculty	faculty	NOUN
cana-5959	2	121	of	of	ADP
cana-5959	2	122	science	science	PROPN
cana-5959	2	123	omar	omar	PROPN
cana-5959	2	124	almukhtar	almukhtar	PROPN
cana-5959	2	125	university	university	PROPN
cana-5959	2	126	,	,	PUNCT
cana-5959	2	127	hnadyalmhdwy708@gmail.com	hnadyalmhdwy708@gmail.com	X
cana-5959	3	1	5assistant	5assistant	NUM
cana-5959	3	2	professor	professor	NOUN
cana-5959	3	3	of	of	ADP
cana-5959	3	4	applied	apply	VERB
cana-5959	3	5	statistics	statistic	NOUN
cana-5959	3	6	,	,	PUNCT
cana-5959	3	7	major	major	ADJ
cana-5959	3	8	of	of	ADP
cana-5959	3	9	statistics	statistic	NOUN
cana-5959	3	10	,	,	PUNCT
cana-5959	3	11	mathematics	mathematics	PROPN
cana-5959	3	12	department	department	PROPN
cana-5959	3	13	faculty	faculty	NOUN
cana-5959	3	14	of	of	ADP
cana-5959	3	15	scienceomar	scienceomar	PROPN
cana-5959	3	16	almukhtar	almukhtar	PROPN
cana-5959	3	17	university	university	PROPN
cana-5959	3	18	,	,	PUNCT
cana-5959	3	19	kasem.abdinibi@gmail.com	kasem.abdinibi@gmail.com	PROPN
cana-5959	3	20	corresponding	corresponding	ADJ
cana-5959	3	21	author	author	NOUN
cana-5959	3	22	:	:	PUNCT
cana-5959	3	23	kasem	kasem	PROPN
cana-5959	3	24	abdinibi	abdinibi	PROPN
cana-5959	3	25	farag	farag	PROPN
cana-5959	3	26	,	,	PUNCT
cana-5959	3	27	assistant	assistant	NOUN
cana-5959	3	28	professor	professor	NOUN
cana-5959	3	29	of	of	ADP
cana-5959	3	30	applied	apply	VERB
cana-5959	3	31	statistics	statistic	NOUN
cana-5959	3	32	,	,	PUNCT
cana-5959	3	33	major	major	ADJ
cana-5959	3	34	of	of	ADP
cana-5959	3	35	statistics	statistic	NOUN
cana-5959	3	36	,	,	PUNCT
cana-5959	3	37	mathematics	mathematics	PROPN
cana-5959	3	38	department	department	PROPN
cana-5959	3	39	faculty	faculty	NOUN
cana-5959	3	40	of	of	ADP
cana-5959	3	41	scienceomar	scienceomar	PROPN
cana-5959	3	42	almukhtar	almukhtar	PROPN
cana-5959	3	43	university	university	PROPN
cana-5959	3	44	,	,	PUNCT
cana-5959	3	45	kasem.abdinibi@gmail.com	kasem.abdinibi@gmail.com	PROPN
cana-5959	3	46	article	article	NOUN
cana-5959	3	47	history	history	NOUN
cana-5959	3	48	:	:	PUNCT
cana-5959	3	49	received	receive	VERB
cana-5959	3	50	:	:	PUNCT
cana-5959	3	51	02	02	NUM
cana-5959	3	52	-	-	PUNCT
cana-5959	3	53	01	01	NUM
cana-5959	3	54	-	-	PUNCT
cana-5959	3	55	2025	2025	NUM
cana-5959	3	56	revised	revise	VERB
cana-5959	3	57	:	:	PUNCT
cana-5959	3	58	25	25	NUM
cana-5959	3	59	-	-	PUNCT
cana-5959	3	60	02	02	NUM
cana-5959	3	61	-	-	PUNCT
cana-5959	3	62	2025	2025	NUM
cana-5959	3	63	accepted	accept	VERB
cana-5959	3	64	:	:	PUNCT
cana-5959	3	65	20	20	NUM
cana-5959	3	66	-	-	SYM
cana-5959	3	67	03	03	NUM
cana-5959	3	68	-	-	PUNCT
cana-5959	3	69	2025	2025	NUM
cana-5959	3	70	abstract	abstract	ADJ
cana-5959	3	71	poisson	poisson	NOUN
cana-5959	3	72	regression	regression	NOUN
cana-5959	3	73	serves	serve	VERB
cana-5959	3	74	as	as	ADP
cana-5959	3	75	a	a	DET
cana-5959	3	76	crucial	crucial	ADJ
cana-5959	3	77	method	method	NOUN
cana-5959	3	78	for	for	ADP
cana-5959	3	79	modeling	model	VERB
cana-5959	3	80	count	count	NOUN
cana-5959	3	81	data	datum	NOUN
cana-5959	3	82	;	;	PUNCT
cana-5959	3	83	however	however	ADV
cana-5959	3	84	,	,	PUNCT
cana-5959	3	85	it	it	PRON
cana-5959	3	86	encounters	encounter	VERB
cana-5959	3	87	challenges	challenge	NOUN
cana-5959	3	88	when	when	SCONJ
cana-5959	3	89	the	the	DET
cana-5959	3	90	data	datum	NOUN
cana-5959	3	91	display	display	NOUN
cana-5959	3	92	overdispersion	overdispersion	NOUN
cana-5959	3	93	,	,	PUNCT
cana-5959	3	94	frequently	frequently	ADV
cana-5959	3	95	due	due	ADJ
cana-5959	3	96	to	to	ADP
cana-5959	3	97	outliers	outlier	NOUN
cana-5959	3	98	,	,	PUNCT
cana-5959	3	99	which	which	PRON
cana-5959	3	100	can	can	AUX
cana-5959	3	101	lead	lead	VERB
cana-5959	3	102	to	to	ADP
cana-5959	3	103	biased	biased	ADJ
cana-5959	3	104	inferences	inference	NOUN
cana-5959	3	105	and	and	CCONJ
cana-5959	3	106	underestimated	underestimate	VERB
cana-5959	3	107	standard	standard	ADJ
cana-5959	3	108	errors	error	NOUN
cana-5959	3	109	.	.	PUNCT
cana-5959	4	1	this	this	DET
cana-5959	4	2	research	research	NOUN
cana-5959	4	3	introduces	introduce	VERB
cana-5959	4	4	an	an	DET
cana-5959	4	5	outlier	outlier	NOUN
cana-5959	4	6	-	-	PUNCT
cana-5959	4	7	weighted	weight	VERB
cana-5959	4	8	poisson	poisson	NOUN
cana-5959	4	9	model	model	NOUN
cana-5959	4	10	(	(	PUNCT
cana-5959	4	11	owpm	owpm	PROPN
cana-5959	4	12	)	)	PUNCT
cana-5959	4	13	that	that	PRON
cana-5959	4	14	utilizes	utilize	VERB
cana-5959	4	15	robust	robust	ADJ
cana-5959	4	16	weights	weight	NOUN
cana-5959	4	17	derived	derive	VERB
cana-5959	4	18	from	from	ADP
cana-5959	4	19	cook	cook	PROPN
cana-5959	4	20	’s	’s	PART
cana-5959	4	21	distance	distance	NOUN
cana-5959	4	22	to	to	PART
cana-5959	4	23	reduce	reduce	VERB
cana-5959	4	24	the	the	DET
cana-5959	4	25	impact	impact	NOUN
cana-5959	4	26	of	of	ADP
cana-5959	4	27	outliers	outlier	NOUN
cana-5959	4	28	.	.	PUNCT
cana-5959	5	1	by	by	ADP
cana-5959	5	2	employing	employ	VERB
cana-5959	5	3	enhanced	enhance	VERB
cana-5959	5	4	simulation	simulation	NOUN
cana-5959	5	5	designs	design	NOUN
cana-5959	5	6	that	that	PRON
cana-5959	5	7	account	account	VERB
cana-5959	5	8	for	for	ADP
cana-5959	5	9	heteroscedasticity	heteroscedasticity	NOUN
cana-5959	5	10	,	,	PUNCT
cana-5959	5	11	zero	zero	NUM
cana-5959	5	12	inflation	inflation	NOUN
cana-5959	5	13	,	,	PUNCT
cana-5959	5	14	and	and	CCONJ
cana-5959	5	15	correlated	correlate	VERB
cana-5959	5	16	predictors	predictor	NOUN
cana-5959	5	17	,	,	PUNCT
cana-5959	5	18	we	we	PRON
cana-5959	5	19	assess	assess	VERB
cana-5959	5	20	the	the	DET
cana-5959	5	21	performance	performance	NOUN
cana-5959	5	22	of	of	ADP
cana-5959	5	23	owpm	owpm	NOUN
cana-5959	5	24	in	in	ADP
cana-5959	5	25	comparison	comparison	NOUN
cana-5959	5	26	to	to	ADP
cana-5959	5	27	standard	standard	ADJ
cana-5959	5	28	poisson	poisson	NOUN
cana-5959	5	29	and	and	CCONJ
cana-5959	5	30	negative	negative	ADJ
cana-5959	5	31	binomial	binomial	ADJ
cana-5959	5	32	models	model	NOUN
cana-5959	5	33	through	through	ADP
cana-5959	5	34	various	various	ADJ
cana-5959	5	35	metrics	metric	NOUN
cana-5959	5	36	and	and	CCONJ
cana-5959	5	37	tests	test	NOUN
cana-5959	5	38	.	.	PUNCT
cana-5959	6	1	the	the	DET
cana-5959	6	2	findings	finding	NOUN
cana-5959	6	3	indicate	indicate	VERB
cana-5959	6	4	that	that	DET
cana-5959	6	5	owpm	owpm	NOUN
cana-5959	6	6	effectively	effectively	ADV
cana-5959	6	7	addresses	address	VERB
cana-5959	6	8	overdispersion	overdispersion	PROPN
cana-5959	6	9	,	,	PUNCT
cana-5959	6	10	resulting	result	VERB
cana-5959	6	11	in	in	ADP
cana-5959	6	12	lower	low	ADJ
cana-5959	6	13	prediction	prediction	NOUN
cana-5959	6	14	errors	error	NOUN
cana-5959	6	15	and	and	CCONJ
cana-5959	6	16	more	more	ADV
cana-5959	6	17	dependable	dependable	ADJ
cana-5959	6	18	inferences	inference	NOUN
cana-5959	6	19	,	,	PUNCT
cana-5959	6	20	akin	akin	ADJ
cana-5959	6	21	to	to	ADP
cana-5959	6	22	those	those	PRON
cana-5959	6	23	obtained	obtain	VERB
cana-5959	6	24	from	from	ADP
cana-5959	6	25	negative	negative	ADJ
cana-5959	6	26	binomial	binomial	ADJ
cana-5959	6	27	regression	regression	NOUN
cana-5959	6	28	.	.	PUNCT
cana-5959	7	1	statistical	statistical	ADJ
cana-5959	7	2	evaluations	evaluation	NOUN
cana-5959	7	3	reveal	reveal	VERB
cana-5959	7	4	significant	significant	ADJ
cana-5959	7	5	enhancements	enhancement	NOUN
cana-5959	7	6	over	over	ADP
cana-5959	7	7	the	the	DET
cana-5959	7	8	conventional	conventional	ADJ
cana-5959	7	9	poisson	poisson	NOUN
cana-5959	7	10	model	model	NOUN
cana-5959	7	11	,	,	PUNCT
cana-5959	7	12	particularly	particularly	ADV
cana-5959	7	13	in	in	ADP
cana-5959	7	14	scenarios	scenario	NOUN
cana-5959	7	15	with	with	ADP
cana-5959	7	16	moderate	moderate	ADJ
cana-5959	7	17	to	to	ADP
cana-5959	7	18	high	high	ADJ
cana-5959	7	19	levels	level	NOUN
cana-5959	7	20	of	of	ADP
cana-5959	7	21	outliers	outlier	NOUN
cana-5959	7	22	.	.	PUNCT
cana-5959	8	1	this	this	DET
cana-5959	8	2	study	study	NOUN
cana-5959	8	3	offers	offer	VERB
cana-5959	8	4	a	a	DET
cana-5959	8	5	practical	practical	ADJ
cana-5959	8	6	and	and	CCONJ
cana-5959	8	7	computationally	computationally	ADV
cana-5959	8	8	efficient	efficient	ADJ
cana-5959	8	9	method	method	NOUN
cana-5959	8	10	for	for	ADP
cana-5959	8	11	robust	robust	ADJ
cana-5959	8	12	regression	regression	NOUN
cana-5959	8	13	of	of	ADP
cana-5959	8	14	count	count	NOUN
cana-5959	8	15	data	datum	NOUN
cana-5959	8	16	,	,	PUNCT
cana-5959	8	17	demonstrating	demonstrate	VERB
cana-5959	8	18	wide	wide	ADV
cana-5959	8	19	-	-	PUNCT
cana-5959	8	20	ranging	range	VERB
cana-5959	8	21	applicability	applicability	NOUN
cana-5959	8	22	.	.	PUNCT
cana-5959	9	1	1	1	X
cana-5959	9	2	.	.	X
cana-5959	9	3	introduction	introduction	NOUN
cana-5959	9	4	count	count	NOUN
cana-5959	9	5	data	datum	NOUN
cana-5959	9	6	frequently	frequently	ADV
cana-5959	9	7	occur	occur	VERB
cana-5959	9	8	in	in	ADP
cana-5959	9	9	disciplines	discipline	NOUN
cana-5959	9	10	such	such	ADJ
cana-5959	9	11	as	as	ADP
cana-5959	9	12	epidemiology	epidemiology	NOUN
cana-5959	9	13	,	,	PUNCT
cana-5959	9	14	ecology	ecology	NOUN
cana-5959	9	15	,	,	PUNCT
cana-5959	9	16	insurance	insurance	NOUN
cana-5959	9	17	,	,	PUNCT
cana-5959	9	18	and	and	CCONJ
cana-5959	9	19	social	social	ADJ
cana-5959	9	20	sciences	science	NOUN
cana-5959	9	21	(	(	PUNCT
cana-5959	9	22	hilbe	hilbe	NOUN
cana-5959	9	23	,	,	PUNCT
cana-5959	9	24	2014	2014	NUM
cana-5959	9	25	;	;	PUNCT
cana-5959	9	26	cameron&trivedi	cameron&trivedi	PROPN
cana-5959	9	27	,	,	PUNCT
cana-5959	9	28	2013	2013	NUM
cana-5959	9	29	)	)	PUNCT
cana-5959	9	30	.	.	PUNCT
cana-5959	10	1	the	the	DET
cana-5959	10	2	classical	classical	ADJ
cana-5959	10	3	method	method	NOUN
cana-5959	10	4	for	for	ADP
cana-5959	10	5	modeling	model	VERB
cana-5959	10	6	such	such	ADJ
cana-5959	10	7	data	datum	NOUN
cana-5959	10	8	mailto:abobaker.jaber@uob.edu.ly	mailto:abobaker.jaber@uob.edu.ly	PROPN
cana-5959	10	9	mailto:moh.mohamed@sebhau.edu.ly	mailto:moh.mohamed@sebhau.edu.ly	NOUN
cana-5959	10	10	mailto:ais.assrhani@sebhau.edu.ly	mailto:ais.assrhani@sebhau.edu.ly	NOUN
cana-5959	11	1	mailto:hnadyalmhdwy708@gmail.com	mailto:hnadyalmhdwy708@gmail.com	X
cana-5959	11	2	mailto:kasem.abdinibi@gmail.com	mailto:kasem.abdinibi@gmail.com	X
cana-5959	11	3	mailto:kasem.abdinibi@gmail.com	mailto:kasem.abdinibi@gmail.com	X
cana-5959	11	4	communications	communication	NOUN
cana-5959	11	5	on	on	ADP
cana-5959	11	6	applied	apply	VERB
cana-5959	11	7	nonlinear	nonlinear	ADJ
cana-5959	11	8	analysis	analysis	NOUN
cana-5959	11	9	issn	issn	NOUN
cana-5959	11	10	:	:	PUNCT
cana-5959	11	11	1074	1074	NUM
cana-5959	11	12	-	-	PUNCT
cana-5959	11	13	133x	133x	NUM
cana-5959	11	14	vol	vol	VERB
cana-5959	11	15	32	32	NUM
cana-5959	11	16	no	no	NOUN
cana-5959	11	17	.	.	PUNCT
cana-5959	12	1	10s	10	NOUN
cana-5959	12	2	(	(	PUNCT
cana-5959	12	3	2025	2025	NUM
cana-5959	12	4	)	)	PUNCT
cana-5959	12	5	3192	3192	NUM
cana-5959	13	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	13	2	is	be	AUX
cana-5959	13	3	poisson	poisson	NOUN
cana-5959	13	4	regression	regression	NOUN
cana-5959	13	5	,	,	PUNCT
cana-5959	13	6	which	which	PRON
cana-5959	13	7	operates	operate	VERB
cana-5959	13	8	under	under	ADP
cana-5959	13	9	the	the	DET
cana-5959	13	10	assumption	assumption	NOUN
cana-5959	13	11	of	of	ADP
cana-5959	13	12	equal	equal	ADJ
cana-5959	13	13	mean	mean	NOUN
cana-5959	13	14	and	and	CCONJ
cana-5959	13	15	variance	variance	NOUN
cana-5959	13	16	(	(	PUNCT
cana-5959	13	17	mccullagh&nelder	mccullagh&nelder	NOUN
cana-5959	13	18	,	,	PUNCT
cana-5959	13	19	1989	1989	NUM
cana-5959	13	20	)	)	PUNCT
cana-5959	13	21	.	.	PUNCT
cana-5959	14	1	however	however	ADV
cana-5959	14	2	,	,	PUNCT
cana-5959	14	3	real	real	ADJ
cana-5959	14	4	-	-	PUNCT
cana-5959	14	5	world	world	NOUN
cana-5959	14	6	datasets	dataset	NOUN
cana-5959	14	7	often	often	ADV
cana-5959	14	8	display	display	VERB
cana-5959	14	9	overdispersion	overdispersion	NOUN
cana-5959	14	10	—	—	PUNCT
cana-5959	14	11	where	where	SCONJ
cana-5959	14	12	variance	variance	NOUN
cana-5959	14	13	surpasses	surpass	VERB
cana-5959	14	14	the	the	DET
cana-5959	14	15	mean	mean	ADJ
cana-5959	14	16	—	—	PUNCT
cana-5959	14	17	thus	thus	ADV
cana-5959	14	18	violating	violate	VERB
cana-5959	14	19	the	the	DET
cana-5959	14	20	assumptions	assumption	NOUN
cana-5959	14	21	of	of	ADP
cana-5959	14	22	poisson	poisson	PROPN
cana-5959	14	23	regression	regression	NOUN
cana-5959	14	24	and	and	CCONJ
cana-5959	14	25	resulting	result	VERB
cana-5959	14	26	in	in	ADP
cana-5959	14	27	underestimated	underestimated	ADJ
cana-5959	14	28	standard	standard	ADJ
cana-5959	14	29	errors	error	NOUN
cana-5959	14	30	and	and	CCONJ
cana-5959	14	31	invalid	invalid	ADJ
cana-5959	14	32	inferences	inference	NOUN
cana-5959	14	33	(	(	PUNCT
cana-5959	14	34	ver	ver	PROPN
cana-5959	14	35	hoef&boveng	hoef&boveng	PROPN
cana-5959	14	36	,	,	PUNCT
cana-5959	14	37	2007	2007	NUM
cana-5959	14	38	;	;	PUNCT
cana-5959	14	39	cameron&trivedi	cameron&trivedi	PROPN
cana-5959	14	40	,	,	PUNCT
cana-5959	14	41	1998	1998	NUM
cana-5959	14	42	)	)	PUNCT
cana-5959	14	43	.	.	PUNCT
cana-5959	15	1	common	common	ADJ
cana-5959	15	2	factors	factor	NOUN
cana-5959	15	3	contributing	contribute	VERB
cana-5959	15	4	to	to	ADP
cana-5959	15	5	overdispersion	overdispersion	NOUN
cana-5959	15	6	include	include	VERB
cana-5959	15	7	unobserved	unobserved	ADJ
cana-5959	15	8	heterogeneity	heterogeneity	NOUN
cana-5959	15	9	,	,	PUNCT
cana-5959	15	10	an	an	DET
cana-5959	15	11	excess	excess	NOUN
cana-5959	15	12	of	of	ADP
cana-5959	15	13	zeros	zero	NOUN
cana-5959	15	14	,	,	PUNCT
cana-5959	15	15	and	and	CCONJ
cana-5959	15	16	particularly	particularly	ADV
cana-5959	15	17	outliers	outlier	NOUN
cana-5959	15	18	(	(	PUNCT
cana-5959	15	19	hausman	hausman	PROPN
cana-5959	15	20	et	et	PROPN
cana-5959	15	21	al	al	PROPN
cana-5959	15	22	.	.	PROPN
cana-5959	15	23	,	,	PUNCT
cana-5959	15	24	1984	1984	NUM
cana-5959	15	25	;	;	PUNCT
cana-5959	15	26	hilbe	hilbe	NOUN
cana-5959	15	27	,	,	PUNCT
cana-5959	15	28	2011	2011	NUM
cana-5959	15	29	)	)	PUNCT
cana-5959	15	30	.	.	PUNCT
cana-5959	16	1	various	various	ADJ
cana-5959	16	2	alternatives	alternative	NOUN
cana-5959	16	3	to	to	ADP
cana-5959	16	4	poisson	poisson	PROPN
cana-5959	16	5	regression	regression	NOUN
cana-5959	16	6	have	have	AUX
cana-5959	16	7	been	be	AUX
cana-5959	16	8	proposed	propose	VERB
cana-5959	16	9	,	,	PUNCT
cana-5959	16	10	such	such	ADJ
cana-5959	16	11	as	as	ADP
cana-5959	16	12	negative	negative	ADJ
cana-5959	16	13	binomial	binomial	ADJ
cana-5959	16	14	(	(	PUNCT
cana-5959	16	15	nb	nb	NOUN
cana-5959	16	16	)	)	PUNCT
cana-5959	16	17	regression	regression	NOUN
cana-5959	16	18	(	(	PUNCT
cana-5959	16	19	lawless	lawless	ADJ
cana-5959	16	20	,	,	PUNCT
cana-5959	16	21	1987	1987	NUM
cana-5959	16	22	)	)	PUNCT
cana-5959	16	23	and	and	CCONJ
cana-5959	16	24	zero	zero	NUM
cana-5959	16	25	-	-	PUNCT
cana-5959	16	26	inflated	inflated	ADJ
cana-5959	16	27	or	or	CCONJ
cana-5959	16	28	hurdle	hurdle	NOUN
cana-5959	16	29	models	model	NOUN
cana-5959	16	30	(	(	PUNCT
cana-5959	16	31	lambert	lambert	PROPN
cana-5959	16	32	,	,	PUNCT
cana-5959	16	33	1992	1992	NUM
cana-5959	16	34	;	;	PUNCT
cana-5959	16	35	zeileis	zeileis	PROPN
cana-5959	16	36	et	et	PROPN
cana-5959	16	37	al	al	PROPN
cana-5959	16	38	.	.	PROPN
cana-5959	16	39	,	,	PUNCT
cana-5959	16	40	2008	2008	NUM
cana-5959	16	41	)	)	PUNCT
cana-5959	16	42	,	,	PUNCT
cana-5959	16	43	each	each	PRON
cana-5959	16	44	targeting	target	VERB
cana-5959	16	45	specific	specific	ADJ
cana-5959	16	46	dimensions	dimension	NOUN
cana-5959	16	47	of	of	ADP
cana-5959	16	48	overdispersion	overdispersion	NOUN
cana-5959	16	49	.	.	PUNCT
cana-5959	17	1	nevertheless	nevertheless	ADV
cana-5959	17	2	,	,	PUNCT
cana-5959	17	3	these	these	DET
cana-5959	17	4	methods	method	NOUN
cana-5959	17	5	may	may	AUX
cana-5959	17	6	not	not	PART
cana-5959	17	7	effectively	effectively	ADV
cana-5959	17	8	downweight	downweight	VERB
cana-5959	17	9	influential	influential	ADJ
cana-5959	17	10	outliers	outlier	NOUN
cana-5959	17	11	,	,	PUNCT
cana-5959	17	12	which	which	PRON
cana-5959	17	13	can	can	AUX
cana-5959	17	14	skew	skew	VERB
cana-5959	17	15	parameter	parameter	NOUN
cana-5959	17	16	estimates	estimate	NOUN
cana-5959	17	17	and	and	CCONJ
cana-5959	17	18	compromise	compromise	NOUN
cana-5959	17	19	model	model	NOUN
cana-5959	17	20	fit	fit	PROPN
cana-5959	17	21	.	.	PUNCT
cana-5959	18	1	robust	robust	ADJ
cana-5959	18	2	regression	regression	NOUN
cana-5959	18	3	methodologies	methodology	NOUN
cana-5959	18	4	have	have	AUX
cana-5959	18	5	been	be	AUX
cana-5959	18	6	extensively	extensively	ADV
cana-5959	18	7	researched	research	VERB
cana-5959	18	8	for	for	ADP
cana-5959	18	9	continuous	continuous	ADJ
cana-5959	18	10	outcomes	outcome	NOUN
cana-5959	18	11	(	(	PUNCT
cana-5959	18	12	rousseeuw&leroy	rousseeuw&leroy	NOUN
cana-5959	18	13	,	,	PUNCT
cana-5959	18	14	1987	1987	NUM
cana-5959	18	15	;	;	PUNCT
cana-5959	18	16	maronna	maronna	PROPN
cana-5959	18	17	et	et	PROPN
cana-5959	18	18	al	al	PROPN
cana-5959	18	19	.	.	PROPN
cana-5959	18	20	,	,	PUNCT
cana-5959	18	21	2019	2019	NUM
cana-5959	18	22	)	)	PUNCT
cana-5959	18	23	,	,	PUNCT
cana-5959	18	24	but	but	CCONJ
cana-5959	18	25	there	there	PRON
cana-5959	18	26	is	be	VERB
cana-5959	18	27	comparatively	comparatively	ADV
cana-5959	18	28	less	less	ADJ
cana-5959	18	29	investigation	investigation	NOUN
cana-5959	18	30	into	into	ADP
cana-5959	18	31	their	their	PRON
cana-5959	18	32	application	application	NOUN
cana-5959	18	33	for	for	ADP
cana-5959	18	34	count	count	NOUN
cana-5959	18	35	data	datum	NOUN
cana-5959	18	36	models	model	NOUN
cana-5959	18	37	.	.	PUNCT
cana-5959	19	1	recently	recently	ADV
cana-5959	19	2	,	,	PUNCT
cana-5959	19	3	weighted	weight	VERB
cana-5959	19	4	poisson	poisson	NOUN
cana-5959	19	5	models	model	NOUN
cana-5959	19	6	that	that	PRON
cana-5959	19	7	utilize	utilize	VERB
cana-5959	19	8	outlier	outlier	NOUN
cana-5959	19	9	-	-	PUNCT
cana-5959	19	10	based	base	VERB
cana-5959	19	11	weights	weight	NOUN
cana-5959	19	12	have	have	AUX
cana-5959	19	13	surfaced	surface	VERB
cana-5959	19	14	as	as	ADP
cana-5959	19	15	promising	promise	VERB
cana-5959	19	16	alternatives	alternative	NOUN
cana-5959	19	17	(	(	PUNCT
cana-5959	19	18	ma	ma	PROPN
cana-5959	19	19	et	et	PROPN
cana-5959	19	20	al	al	PROPN
cana-5959	19	21	.	.	PROPN
cana-5959	19	22	,	,	PUNCT
cana-5959	19	23	2017	2017	NUM
cana-5959	19	24	;	;	PUNCT
cana-5959	19	25	jin	jin	PROPN
cana-5959	19	26	et	et	PROPN
cana-5959	19	27	al	al	PROPN
cana-5959	19	28	.	.	PROPN
cana-5959	19	29	,	,	PUNCT
cana-5959	19	30	2020	2020	NUM
cana-5959	19	31	)	)	PUNCT
cana-5959	19	32	.	.	PUNCT
cana-5959	20	1	these	these	DET
cana-5959	20	2	techniques	technique	NOUN
cana-5959	20	3	identify	identify	VERB
cana-5959	20	4	influential	influential	ADJ
cana-5959	20	5	observations	observation	NOUN
cana-5959	20	6	through	through	ADP
cana-5959	20	7	diagnostic	diagnostic	ADJ
cana-5959	20	8	metrics	metric	NOUN
cana-5959	20	9	such	such	ADJ
cana-5959	20	10	as	as	ADP
cana-5959	20	11	cook	cook	NOUN
cana-5959	20	12	’s	’s	PART
cana-5959	20	13	distance	distance	NOUN
cana-5959	20	14	and	and	CCONJ
cana-5959	20	15	apply	apply	VERB
cana-5959	20	16	downweighting	downweighte	VERB
cana-5959	20	17	to	to	PART
cana-5959	20	18	mitigate	mitigate	VERB
cana-5959	20	19	bias	bias	NOUN
cana-5959	20	20	.	.	PUNCT
cana-5959	21	1	research	research	NOUN
cana-5959	21	2	gap	gap	NOUN
cana-5959	21	3	:	:	PUNCT
cana-5959	21	4	in	in	ADP
cana-5959	21	5	spite	spite	NOUN
cana-5959	21	6	of	of	ADP
cana-5959	21	7	the	the	DET
cana-5959	21	8	availability	availability	NOUN
cana-5959	21	9	of	of	ADP
cana-5959	21	10	robust	robust	ADJ
cana-5959	21	11	count	count	NOUN
cana-5959	21	12	regression	regression	NOUN
cana-5959	21	13	models	model	NOUN
cana-5959	21	14	,	,	PUNCT
cana-5959	21	15	there	there	PRON
cana-5959	21	16	is	be	VERB
cana-5959	21	17	a	a	DET
cana-5959	21	18	scarcity	scarcity	NOUN
cana-5959	21	19	of	of	ADP
cana-5959	21	20	studies	study	NOUN
cana-5959	21	21	that	that	PRON
cana-5959	21	22	have	have	AUX
cana-5959	21	23	systematically	systematically	ADV
cana-5959	21	24	assessed	assess	VERB
cana-5959	21	25	their	their	PRON
cana-5959	21	26	efficacy	efficacy	NOUN
cana-5959	21	27	under	under	ADP
cana-5959	21	28	realistic	realistic	ADJ
cana-5959	21	29	data	data	NOUN
cana-5959	21	30	-	-	PUNCT
cana-5959	21	31	generating	generate	VERB
cana-5959	21	32	conditions	condition	NOUN
cana-5959	21	33	,	,	PUNCT
cana-5959	21	34	including	include	VERB
cana-5959	21	35	heteroscedasticity	heteroscedasticity	NOUN
cana-5959	21	36	,	,	PUNCT
cana-5959	21	37	zero	zero	NUM
cana-5959	21	38	inflation	inflation	NOUN
cana-5959	21	39	,	,	PUNCT
cana-5959	21	40	and	and	CCONJ
cana-5959	21	41	correlated	correlate	VERB
cana-5959	21	42	predictors	predictor	NOUN
cana-5959	21	43	,	,	PUNCT
cana-5959	21	44	with	with	ADP
cana-5959	21	45	a	a	DET
cana-5959	21	46	particular	particular	ADJ
cana-5959	21	47	focus	focus	NOUN
cana-5959	21	48	on	on	ADP
cana-5959	21	49	the	the	DET
cana-5959	21	50	influence	influence	NOUN
cana-5959	21	51	of	of	ADP
cana-5959	21	52	outliers	outlier	NOUN
cana-5959	21	53	on	on	ADP
cana-5959	21	54	overdispersion	overdispersion	NOUN
cana-5959	21	55	.	.	PUNCT
cana-5959	22	1	objective	objective	NOUN
cana-5959	22	2	:	:	PUNCT
cana-5959	22	3	this	this	DET
cana-5959	22	4	paper	paper	NOUN
cana-5959	22	5	aims	aim	VERB
cana-5959	22	6	to	to	PART
cana-5959	22	7	develop	develop	VERB
cana-5959	22	8	an	an	DET
cana-5959	22	9	outlier	outlier	NOUN
cana-5959	22	10	-	-	PUNCT
cana-5959	22	11	weighted	weight	VERB
cana-5959	22	12	poisson	poisson	NOUN
cana-5959	22	13	model	model	NOUN
cana-5959	22	14	(	(	PUNCT
cana-5959	22	15	owpm	owpm	PROPN
cana-5959	22	16	)	)	PUNCT
cana-5959	22	17	that	that	PRON
cana-5959	22	18	utilizes	utilize	VERB
cana-5959	22	19	cook	cook	PROPN
cana-5959	22	20	’s	’s	PART
cana-5959	22	21	distance	distance	NOUN
cana-5959	22	22	weights	weight	NOUN
cana-5959	22	23	and	and	CCONJ
cana-5959	22	24	compares	compare	VERB
cana-5959	22	25	its	its	PRON
cana-5959	22	26	performance	performance	NOUN
cana-5959	22	27	against	against	ADP
cana-5959	22	28	standard	standard	ADJ
cana-5959	22	29	poisson	poisson	NOUN
cana-5959	22	30	and	and	CCONJ
cana-5959	22	31	negative	negative	ADJ
cana-5959	22	32	binomial	binomial	ADJ
cana-5959	22	33	models	model	NOUN
cana-5959	22	34	through	through	ADP
cana-5959	22	35	comprehensive	comprehensive	ADJ
cana-5959	22	36	simulation	simulation	NOUN
cana-5959	22	37	.	.	PUNCT
cana-5959	23	1	performance	performance	NOUN
cana-5959	23	2	evaluation	evaluation	NOUN
cana-5959	23	3	is	be	AUX
cana-5959	23	4	conducted	conduct	VERB
cana-5959	23	5	through	through	ADP
cana-5959	23	6	various	various	ADJ
cana-5959	23	7	metrics	metric	NOUN
cana-5959	23	8	and	and	CCONJ
cana-5959	23	9	graphical	graphical	ADJ
cana-5959	23	10	diagnostics	diagnostic	NOUN
cana-5959	23	11	,	,	PUNCT
cana-5959	23	12	while	while	SCONJ
cana-5959	23	13	statistical	statistical	ADJ
cana-5959	23	14	testing	testing	NOUN
cana-5959	23	15	serves	serve	VERB
cana-5959	23	16	to	to	PART
cana-5959	23	17	confirm	confirm	VERB
cana-5959	23	18	enhancements	enhancement	NOUN
cana-5959	23	19	.	.	PUNCT
cana-5959	24	1	2	2	X
cana-5959	24	2	.	.	X
cana-5959	24	3	methodology	methodology	NOUN
cana-5959	24	4	2.1	2.1	NUM
cana-5959	24	5	problem	problem	NOUN
cana-5959	24	6	statement	statement	NOUN
cana-5959	24	7	and	and	CCONJ
cana-5959	24	8	overdispersion	overdispersion	NOUN
cana-5959	24	9	testing	testing	NOUN
cana-5959	24	10	standard	standard	ADJ
cana-5959	24	11	poisson	poisson	NOUN
cana-5959	24	12	regression	regression	NOUN
cana-5959	24	13	assumes	assume	VERB
cana-5959	24	14	:	:	PUNCT
cana-5959	24	15	.	.	PUNCT
cana-5959	25	1	overdispersion	overdispersion	NOUN
cana-5959	25	2	occurs	occur	VERB
cana-5959	25	3	if	if	SCONJ
cana-5959	25	4	where	where	SCONJ
cana-5959	25	5	is	be	AUX
cana-5959	25	6	the	the	DET
cana-5959	25	7	number	number	NOUN
cana-5959	25	8	of	of	ADP
cana-5959	25	9	predictors	predictor	NOUN
cana-5959	25	10	.	.	PUNCT
cana-5959	26	1	ignoring	ignore	VERB
cana-5959	26	2	overdispersion	overdispersion	NOUN
cana-5959	26	3	inflates	inflate	NOUN
cana-5959	26	4	type	type	NOUN
cana-5959	26	5	errors	error	NOUN
cana-5959	26	6	and	and	CCONJ
cana-5959	26	7	produces	produce	VERB
cana-5959	26	8	misleading	misleading	ADJ
cana-5959	26	9	inference	inference	NOUN
cana-5959	26	10	(	(	PUNCT
cana-5959	26	11	dean	dean	PROPN
cana-5959	26	12	,	,	PUNCT
cana-5959	26	13	1992	1992	NUM
cana-5959	26	14	;	;	PUNCT
cana-5959	26	15	hilbe	hilbe	NOUN
cana-5959	26	16	,	,	PUNCT
cana-5959	26	17	2014	2014	NUM
cana-5959	26	18	)	)	PUNCT
cana-5959	26	19	.	.	PUNCT
cana-5959	27	1	regression	regression	NOUN
cana-5959	27	2	-	-	PUNCT
cana-5959	27	3	based	base	VERB
cana-5959	27	4	tests	test	NOUN
cana-5959	27	5	such	such	ADJ
cana-5959	27	6	as	as	ADP
cana-5959	27	7	communications	communication	NOUN
cana-5959	27	8	on	on	ADP
cana-5959	27	9	applied	apply	VERB
cana-5959	27	10	nonlinear	nonlinear	ADJ
cana-5959	27	11	analysis	analysis	NOUN
cana-5959	27	12	issn	issn	NOUN
cana-5959	27	13	:	:	PUNCT
cana-5959	27	14	1074	1074	NUM
cana-5959	27	15	-	-	PUNCT
cana-5959	27	16	133x	133x	NUM
cana-5959	27	17	vol	vol	VERB
cana-5959	27	18	32	32	NUM
cana-5959	27	19	no	no	NOUN
cana-5959	27	20	.	.	PUNCT
cana-5959	28	1	10s	10	NOUN
cana-5959	28	2	(	(	PUNCT
cana-5959	28	3	2025	2025	NUM
cana-5959	28	4	)	)	PUNCT
cana-5959	28	5	3193	3193	NUM
cana-5959	28	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	28	7	cameron	cameron	PROPN
cana-5959	28	8	&	&	CCONJ
cana-5959	28	9	trivedi	trivedi	PROPN
cana-5959	28	10	’s	’s	PART
cana-5959	28	11	test	test	NOUN
cana-5959	28	12	or	or	CCONJ
cana-5959	28	13	the	the	DET
cana-5959	28	14	t	t	NOUN
cana-5959	28	15	-	-	PUNCT
cana-5959	28	16	test	test	NOUN
cana-5959	28	17	on	on	ADP
cana-5959	28	18	pearson	pearson	PROPN
cana-5959	28	19	residuals	residual	NOUN
cana-5959	28	20	detect	detect	VERB
cana-5959	28	21	overdispersion	overdispersion	NOUN
cana-5959	28	22	but	but	CCONJ
cana-5959	28	23	do	do	AUX
cana-5959	28	24	not	not	PART
cana-5959	28	25	correct	correct	VERB
cana-5959	28	26	it	it	PRON
cana-5959	28	27	(	(	PUNCT
cana-5959	28	28	cameron&trivedi	cameron&trivedi	PROPN
cana-5959	28	29	,	,	PUNCT
cana-5959	28	30	1990	1990	NUM
cana-5959	28	31	)	)	PUNCT
cana-5959	28	32	.	.	PUNCT
cana-5959	29	1	2.2	2.2	NUM
cana-5959	29	2	proposed	propose	VERB
cana-5959	29	3	outlier	outlier	NOUN
cana-5959	29	4	-	-	PUNCT
cana-5959	29	5	weighted	weight	VERB
cana-5959	29	6	poisson	poisson	NOUN
cana-5959	29	7	model	model	NOUN
cana-5959	29	8	(	(	PUNCT
cana-5959	29	9	owpm	owpm	PROPN
cana-5959	29	10	)	)	PUNCT
cana-5959	29	11	we	we	PRON
cana-5959	29	12	propose	propose	VERB
cana-5959	29	13	an	an	DET
cana-5959	29	14	iterative	iterative	NOUN
cana-5959	29	15	weighting	weighting	NOUN
cana-5959	29	16	scheme	scheme	NOUN
cana-5959	29	17	:	:	PUNCT
cana-5959	29	18	⚫	⚫	NOUN
cana-5959	29	19	fit	fit	VERB
cana-5959	29	20	a	a	DET
cana-5959	29	21	standard	standard	ADJ
cana-5959	29	22	poisson	poisson	NOUN
cana-5959	29	23	model	model	NOUN
cana-5959	29	24	and	and	CCONJ
cana-5959	29	25	calculate	calculate	VERB
cana-5959	29	26	cook	cook	PROPN
cana-5959	29	27	’s	’s	PART
cana-5959	29	28	distance	distance	NOUN
cana-5959	29	29	for	for	ADP
cana-5959	29	30	each	each	DET
cana-5959	29	31	observation	observation	NOUN
cana-5959	29	32	.	.	PUNCT
cana-5959	30	1	⚫	⚫	NOUN
cana-5959	30	2	define	define	VERB
cana-5959	30	3	weights	weight	NOUN
cana-5959	30	4	via	via	ADP
cana-5959	30	5	a	a	DET
cana-5959	30	6	tukey	tukey	NOUN
cana-5959	30	7	’s	’s	PART
cana-5959	30	8	biweight	biweight	ADJ
cana-5959	30	9	function	function	NOUN
cana-5959	30	10	on	on	ADP
cana-5959	30	11	cook	cook	PROPN
cana-5959	30	12	’s	’s	PART
cana-5959	30	13	distances	distance	NOUN
cana-5959	30	14	to	to	ADP
cana-5959	30	15	downweight	downweight	ADJ
cana-5959	30	16	influential	influential	ADJ
cana-5959	30	17	outliers	outlier	NOUN
cana-5959	30	18	(	(	PUNCT
cana-5959	30	19	rousseeuw&leroy	rousseeuw&leroy	NOUN
cana-5959	30	20	,	,	PUNCT
cana-5959	30	21	1987	1987	NUM
cana-5959	30	22	)	)	PUNCT
cana-5959	30	23	.	.	PUNCT
cana-5959	31	1	⚫	⚫	NOUN
cana-5959	31	2	refit	refit	NOUN
cana-5959	31	3	the	the	DET
cana-5959	31	4	poisson	poisson	PROPN
cana-5959	31	5	model	model	NOUN
cana-5959	31	6	using	use	VERB
cana-5959	31	7	these	these	DET
cana-5959	31	8	weights	weight	NOUN
cana-5959	31	9	,	,	PUNCT
cana-5959	31	10	mitigating	mitigate	VERB
cana-5959	31	11	outlier	outlier	NOUN
cana-5959	31	12	impact	impact	NOUN
cana-5959	31	13	.	.	PUNCT
cana-5959	32	1	⚫	⚫	NOUN
cana-5959	32	2	this	this	DET
cana-5959	32	3	approach	approach	NOUN
cana-5959	32	4	builds	build	VERB
cana-5959	32	5	on	on	ADP
cana-5959	32	6	robust	robust	ADJ
cana-5959	32	7	methods	method	NOUN
cana-5959	32	8	in	in	ADP
cana-5959	32	9	generalized	generalized	ADJ
cana-5959	32	10	linear	linear	NOUN
cana-5959	32	11	models	model	NOUN
cana-5959	32	12	(	(	PUNCT
cana-5959	32	13	müller&welsh	müller&welsh	NOUN
cana-5959	32	14	,	,	PUNCT
cana-5959	32	15	2005	2005	NUM
cana-5959	32	16	;	;	PUNCT
cana-5959	32	17	gervini&yohai	gervini&yohai	X
cana-5959	32	18	,	,	PUNCT
cana-5959	32	19	2002	2002	NUM
cana-5959	32	20	)	)	PUNCT
cana-5959	32	21	and	and	CCONJ
cana-5959	32	22	adapts	adapt	VERB
cana-5959	32	23	them	they	PRON
cana-5959	32	24	for	for	ADP
cana-5959	32	25	count	count	NOUN
cana-5959	32	26	data	datum	NOUN
cana-5959	32	27	.	.	PUNCT
cana-5959	33	1	2.3	2.3	NUM
cana-5959	33	2	simulation	simulation	NOUN
cana-5959	33	3	design	design	NOUN
cana-5959	33	4	to	to	ADP
cana-5959	33	5	realistically	realistically	ADV
cana-5959	33	6	mimic	mimic	VERB
cana-5959	33	7	count	count	NOUN
cana-5959	33	8	data	datum	NOUN
cana-5959	33	9	characteristics	characteristic	NOUN
cana-5959	33	10	,	,	PUNCT
cana-5959	33	11	our	our	PRON
cana-5959	33	12	simulation	simulation	NOUN
cana-5959	33	13	incorporates	incorporate	VERB
cana-5959	33	14	:	:	PUNCT
cana-5959	33	15	⚫	⚫	NOUN
cana-5959	33	16	correlated	correlate	VERB
cana-5959	33	17	predictors	predictor	NOUN
cana-5959	33	18	generated	generate	VERB
cana-5959	33	19	via	via	ADP
cana-5959	33	20	multivariate	multivariate	NOUN
cana-5959	33	21	normal	normal	ADJ
cana-5959	33	22	.	.	PUNCT
cana-5959	34	1	⚫	⚫	VERB
cana-5959	34	2	heteroscedastic	heteroscedastic	ADJ
cana-5959	34	3	mean	mean	NOUN
cana-5959	34	4	:	:	PUNCT
cana-5959	34	5	⚫	⚫	NOUN
cana-5959	34	6	zero	zero	NUM
cana-5959	34	7	inflation	inflation	NOUN
cana-5959	34	8	(	(	PUNCT
cana-5959	34	9	10	10	NUM
cana-5959	34	10	%	%	NOUN
cana-5959	34	11	)	)	PUNCT
cana-5959	34	12	to	to	PART
cana-5959	34	13	simulate	simulate	VERB
cana-5959	34	14	excess	excess	ADJ
cana-5959	34	15	zeros	zero	NOUN
cana-5959	34	16	(	(	PUNCT
cana-5959	34	17	lambert	lambert	PROPN
cana-5959	34	18	,	,	PUNCT
cana-5959	34	19	1992	1992	NUM
cana-5959	34	20	)	)	PUNCT
cana-5959	34	21	.	.	PUNCT
cana-5959	35	1	⚫	⚫	NOUN
cana-5959	35	2	outliers	outlier	NOUN
cana-5959	35	3	introduced	introduce	VERB
cana-5959	35	4	as	as	ADP
cana-5959	35	5	extreme	extreme	ADJ
cana-5959	35	6	counts	count	NOUN
cana-5959	35	7	added	add	VERB
cana-5959	35	8	/	/	PUNCT
cana-5959	35	9	subtracted	subtract	VERB
cana-5959	35	10	with	with	ADP
cana-5959	35	11	heavy	heavy	ADJ
cana-5959	35	12	-	-	PUNCT
cana-5959	35	13	tailed	tail	VERB
cana-5959	35	14	magnitudes	magnitude	NOUN
cana-5959	35	15	.	.	PUNCT
cana-5959	36	1	⚫	⚫	VERB
cana-5959	36	2	two	two	NUM
cana-5959	36	3	regression	regression	NOUN
cana-5959	36	4	types	type	NOUN
cana-5959	36	5	:	:	PUNCT
cana-5959	36	6	simple	simple	ADJ
cana-5959	36	7	(	(	PUNCT
cana-5959	36	8	one	one	NUM
cana-5959	36	9	predictor	predictor	NOUN
cana-5959	36	10	)	)	PUNCT
cana-5959	36	11	and	and	CCONJ
cana-5959	36	12	multiple	multiple	ADJ
cana-5959	36	13	(	(	PUNCT
cana-5959	36	14	two	two	NUM
cana-5959	36	15	predictors	predictor	NOUN
cana-5959	36	16	)	)	PUNCT
cana-5959	36	17	.	.	PUNCT
cana-5959	37	1	3	3	X
cana-5959	37	2	.	.	X
cana-5959	37	3	comparative	comparative	ADJ
cana-5959	37	4	models	model	NOUN
cana-5959	37	5	in	in	ADP
cana-5959	37	6	this	this	DET
cana-5959	37	7	research	research	NOUN
cana-5959	37	8	,	,	PUNCT
cana-5959	37	9	we	we	PRON
cana-5959	37	10	conduct	conduct	VERB
cana-5959	37	11	a	a	DET
cana-5959	37	12	systematic	systematic	ADJ
cana-5959	37	13	comparison	comparison	NOUN
cana-5959	37	14	of	of	ADP
cana-5959	37	15	three	three	NUM
cana-5959	37	16	distinct	distinct	ADJ
cana-5959	37	17	modeling	modeling	NOUN
cana-5959	37	18	techniques	technique	NOUN
cana-5959	37	19	for	for	ADP
cana-5959	37	20	managing	manage	VERB
cana-5959	37	21	count	count	NOUN
cana-5959	37	22	data	datum	NOUN
cana-5959	37	23	characterized	characterize	VERB
cana-5959	37	24	by	by	ADP
cana-5959	37	25	overdispersion	overdispersion	NOUN
cana-5959	37	26	and	and	CCONJ
cana-5959	37	27	the	the	DET
cana-5959	37	28	presence	presence	NOUN
cana-5959	37	29	of	of	ADP
cana-5959	37	30	outliers	outlier	NOUN
cana-5959	37	31	.	.	PUNCT
cana-5959	38	1	the	the	DET
cana-5959	38	2	models	model	NOUN
cana-5959	38	3	examined	examine	VERB
cana-5959	38	4	include	include	VERB
cana-5959	38	5	:	:	PUNCT
cana-5959	38	6	the	the	DET
cana-5959	38	7	standard	standard	ADJ
cana-5959	38	8	poisson	poisson	NOUN
cana-5959	38	9	(	(	PUNCT
cana-5959	38	10	sp	sp	NOUN
cana-5959	38	11	)	)	PUNCT
cana-5959	38	12	regression	regression	NOUN
cana-5959	38	13	model	model	NOUN
cana-5959	38	14	,	,	PUNCT
cana-5959	38	15	which	which	PRON
cana-5959	38	16	serves	serve	VERB
cana-5959	38	17	as	as	ADP
cana-5959	38	18	a	a	DET
cana-5959	38	19	baseline	baseline	NOUN
cana-5959	38	20	,	,	PUNCT
cana-5959	38	21	a	a	DET
cana-5959	38	22	novel	novel	NOUN
cana-5959	38	23	overdispersion	overdispersion	NOUN
cana-5959	38	24	-	-	PUNCT
cana-5959	38	25	weighted	weight	VERB
cana-5959	38	26	poisson	poisson	NOUN
cana-5959	38	27	model	model	NOUN
cana-5959	38	28	(	(	PUNCT
cana-5959	38	29	owpm	owpm	PROPN
cana-5959	38	30	)	)	PUNCT
cana-5959	38	31	introduced	introduce	VERB
cana-5959	38	32	in	in	ADP
cana-5959	38	33	this	this	DET
cana-5959	38	34	study	study	NOUN
cana-5959	38	35	,	,	PUNCT
cana-5959	38	36	and	and	CCONJ
cana-5959	38	37	the	the	DET
cana-5959	38	38	commonly	commonly	ADV
cana-5959	38	39	utilized	utilize	VERB
cana-5959	38	40	negative	negative	ADJ
cana-5959	38	41	binomial	binomial	ADJ
cana-5959	38	42	(	(	PUNCT
cana-5959	38	43	nb	nb	NOUN
cana-5959	38	44	)	)	PUNCT
cana-5959	38	45	regression	regression	NOUN
cana-5959	38	46	model	model	NOUN
cana-5959	38	47	,	,	PUNCT
cana-5959	38	48	which	which	PRON
cana-5959	38	49	is	be	AUX
cana-5959	38	50	recognized	recognize	VERB
cana-5959	38	51	as	as	ADP
cana-5959	38	52	a	a	DET
cana-5959	38	53	conventional	conventional	ADJ
cana-5959	38	54	method	method	NOUN
cana-5959	38	55	for	for	ADP
cana-5959	38	56	correcting	correct	VERB
cana-5959	38	57	overdispersion	overdispersion	NOUN
cana-5959	38	58	.	.	PUNCT
cana-5959	39	1	3.1	3.1	NUM
cana-5959	39	2	standard	standard	ADJ
cana-5959	39	3	poisson	poisson	NOUN
cana-5959	39	4	model	model	NOUN
cana-5959	39	5	(	(	PUNCT
cana-5959	39	6	sp	sp	NOUN
cana-5959	39	7	)	)	PUNCT
cana-5959	39	8	the	the	DET
cana-5959	39	9	poisson	poisson	PROPN
cana-5959	39	10	regression	regression	NOUN
cana-5959	39	11	model	model	NOUN
cana-5959	39	12	is	be	AUX
cana-5959	39	13	traditionally	traditionally	ADV
cana-5959	39	14	utilized	utilize	VERB
cana-5959	39	15	for	for	ADP
cana-5959	39	16	the	the	DET
cana-5959	39	17	analysis	analysis	NOUN
cana-5959	39	18	of	of	ADP
cana-5959	39	19	count	count	NOUN
cana-5959	39	20	data	datum	NOUN
cana-5959	39	21	,	,	PUNCT
cana-5959	39	22	positing	posit	VERB
cana-5959	39	23	that	that	SCONJ
cana-5959	39	24	the	the	DET
cana-5959	39	25	response	response	NOUN
cana-5959	39	26	variable	variable	NOUN
cana-5959	39	27	adheres	adhere	VERB
cana-5959	39	28	to	to	ADP
cana-5959	39	29	a	a	DET
cana-5959	39	30	poisson	poisson	NOUN
cana-5959	39	31	distribution	distribution	NOUN
cana-5959	39	32	contingent	contingent	NOUN
cana-5959	39	33	upon	upon	SCONJ
cana-5959	39	34	covariates	covariate	NOUN
cana-5959	39	35	:	:	PUNCT
cana-5959	39	36	this	this	DET
cana-5959	39	37	model	model	NOUN
cana-5959	39	38	presumes	presume	VERB
cana-5959	39	39	equidispersion	equidispersion	NOUN
cana-5959	39	40	,	,	PUNCT
cana-5959	39	41	meaning	mean	VERB
cana-5959	39	42	that	that	SCONJ
cana-5959	39	43	:	:	PUNCT
cana-5959	39	44	communications	communication	NOUN
cana-5959	39	45	on	on	ADP
cana-5959	39	46	applied	apply	VERB
cana-5959	39	47	nonlinear	nonlinear	ADJ
cana-5959	39	48	analysis	analysis	NOUN
cana-5959	39	49	issn	issn	NOUN
cana-5959	39	50	:	:	PUNCT
cana-5959	39	51	1074	1074	NUM
cana-5959	39	52	-	-	PUNCT
cana-5959	39	53	133x	133x	NUM
cana-5959	39	54	vol	vol	VERB
cana-5959	39	55	32	32	NUM
cana-5959	39	56	no	no	NOUN
cana-5959	39	57	.	.	PUNCT
cana-5959	40	1	10s	10	NOUN
cana-5959	40	2	(	(	PUNCT
cana-5959	40	3	2025	2025	NUM
cana-5959	40	4	)	)	PUNCT
cana-5959	40	5	3194	3194	NUM
cana-5959	40	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	41	1	although	although	SCONJ
cana-5959	41	2	this	this	DET
cana-5959	41	3	assumption	assumption	NOUN
cana-5959	41	4	is	be	AUX
cana-5959	41	5	practical	practical	ADJ
cana-5959	41	6	and	and	CCONJ
cana-5959	41	7	easy	easy	ADJ
cana-5959	41	8	to	to	PART
cana-5959	41	9	interpret	interpret	VERB
cana-5959	41	10	,	,	PUNCT
cana-5959	41	11	it	it	PRON
cana-5959	41	12	seldom	seldom	ADV
cana-5959	41	13	holds	hold	VERB
cana-5959	41	14	true	true	ADJ
cana-5959	41	15	in	in	ADP
cana-5959	41	16	real	real	ADJ
cana-5959	41	17	-	-	PUNCT
cana-5959	41	18	world	world	NOUN
cana-5959	41	19	scenarios	scenario	NOUN
cana-5959	41	20	due	due	ADJ
cana-5959	41	21	to	to	ADP
cana-5959	41	22	latent	latent	ADJ
cana-5959	41	23	heterogeneity	heterogeneity	NOUN
cana-5959	41	24	or	or	CCONJ
cana-5959	41	25	unobserved	unobserved	ADJ
cana-5959	41	26	variables	variable	NOUN
cana-5959	41	27	(	(	PUNCT
cana-5959	41	28	cameron&trivedi	cameron&trivedi	PROPN
cana-5959	41	29	,	,	PUNCT
cana-5959	41	30	2013	2013	NUM
cana-5959	41	31	)	)	PUNCT
cana-5959	41	32	.	.	PUNCT
cana-5959	42	1	in	in	ADP
cana-5959	42	2	instances	instance	NOUN
cana-5959	42	3	of	of	ADP
cana-5959	42	4	overdispersion	overdispersion	NOUN
cana-5959	42	5	present	present	ADJ
cana-5959	42	6	:	:	PUNCT
cana-5959	42	7	standard	standard	ADJ
cana-5959	42	8	poisson	poisson	NOUN
cana-5959	42	9	regression	regression	NOUN
cana-5959	42	10	tends	tend	VERB
cana-5959	42	11	to	to	PART
cana-5959	42	12	underestimate	underestimate	VERB
cana-5959	42	13	standard	standard	ADJ
cana-5959	42	14	errors	error	NOUN
cana-5959	42	15	,	,	PUNCT
cana-5959	42	16	which	which	PRON
cana-5959	42	17	can	can	AUX
cana-5959	42	18	result	result	VERB
cana-5959	42	19	in	in	ADP
cana-5959	42	20	m	m	PROPN
cana-5959	43	1	i	i	NOUN
cana-5959	43	2	s	s	PART
cana-5959	43	3	l	l	NOUN
cana-5959	43	4	e	e	NOUN
cana-5959	44	1	a	a	X
cana-5959	45	1	d	d	X
cana-5959	46	1	i	i	PRON
cana-5959	47	1	n	n	CCONJ
cana-5959	48	1	g	g	PROPN
cana-5959	48	2	s	s	PROPN
cana-5959	48	3	t	t	PROPN
cana-5959	48	4	a	a	DET
cana-5959	48	5	t	t	NOUN
cana-5959	49	1	i	i	PRON
cana-5959	49	2	s	s	VERB
cana-5959	49	3	t	t	X
cana-5959	50	1	i	i	PRON
cana-5959	50	2	c	c	VERB
cana-5959	50	3	a	a	DET
cana-5959	50	4	l	l	NOUN
cana-5959	50	5	s	s	VERB
cana-5959	50	6	i	i	PRON
cana-5959	50	7	g	g	PROPN
cana-5959	51	1	n	n	INTJ
cana-5959	52	1	i	i	PRON
cana-5959	52	2	f	f	VERB
cana-5959	53	1	i	i	PRON
cana-5959	53	2	c	c	VERB
cana-5959	53	3	a	a	DET
cana-5959	53	4	n	n	NOUN
cana-5959	53	5	c	c	NOUN
cana-5959	53	6	e	e	X
cana-5959	53	7	(	(	PUNCT
cana-5959	53	8	d	d	PROPN
cana-5959	53	9	e	e	PROPN
cana-5959	53	10	a	a	DET
cana-5959	53	11	n	n	NOUN
cana-5959	53	12	,	,	PUNCT
cana-5959	53	13	1	1	NUM
cana-5959	53	14	9	9	NUM
cana-5959	53	15	9	9	NUM
cana-5959	53	16	2	2	NUM
cana-5959	53	17	;	;	PUNCT
cana-5959	54	1	h	h	NOUN
cana-5959	55	1	i	i	NOUN
cana-5959	55	2	l	l	NOUN
cana-5959	55	3	b	b	PROPN
cana-5959	55	4	e	e	X
cana-5959	55	5	,	,	PUNCT
cana-5959	55	6	2	2	NUM
cana-5959	55	7	0	0	NUM
cana-5959	55	8	1	1	NUM
cana-5959	55	9	1	1	NUM
cana-5959	55	10	)	)	PUNCT
cana-5959	55	11	.	.	PUNCT
cana-5959	56	1	3.2	3.2	NUM
cana-5959	56	2	overdispersion	overdispersion	NOUN
cana-5959	56	3	-	-	PUNCT
cana-5959	56	4	weighted	weight	VERB
cana-5959	56	5	poisson	poisson	NOUN
cana-5959	56	6	model	model	NOUN
cana-5959	56	7	(	(	PUNCT
cana-5959	56	8	owpm	owpm	PROPN
cana-5959	56	9	)	)	PUNCT
cana-5959	56	10	to	to	PART
cana-5959	56	11	tackle	tackle	VERB
cana-5959	56	12	the	the	DET
cana-5959	56	13	issue	issue	NOUN
cana-5959	56	14	of	of	ADP
cana-5959	56	15	overdispersion	overdispersion	NOUN
cana-5959	56	16	primarily	primarily	ADV
cana-5959	56	17	caused	cause	VERB
cana-5959	56	18	by	by	ADP
cana-5959	56	19	outlier	outlier	NOUN
cana-5959	56	20	contamination	contamination	NOUN
cana-5959	56	21	,	,	PUNCT
cana-5959	56	22	we	we	PRON
cana-5959	56	23	introduce	introduce	VERB
cana-5959	56	24	a	a	DET
cana-5959	56	25	robust	robust	ADJ
cana-5959	56	26	enhancement	enhancement	NOUN
cana-5959	56	27	to	to	ADP
cana-5959	56	28	the	the	DET
cana-5959	56	29	conventional	conventional	ADJ
cana-5959	56	30	poisson	poisson	NOUN
cana-5959	56	31	model	model	NOUN
cana-5959	56	32	through	through	ADP
cana-5959	56	33	a	a	DET
cana-5959	56	34	weighting	weighting	NOUN
cana-5959	56	35	scheme	scheme	NOUN
cana-5959	56	36	informed	inform	VERB
cana-5959	56	37	by	by	ADP
cana-5959	56	38	influence	influence	NOUN
cana-5959	56	39	diagnostics	diagnostic	NOUN
cana-5959	56	40	.	.	PUNCT
cana-5959	57	1	drawing	draw	VERB
cana-5959	57	2	inspiration	inspiration	NOUN
cana-5959	57	3	from	from	ADP
cana-5959	57	4	robust	robust	ADJ
cana-5959	57	5	regression	regression	NOUN
cana-5959	57	6	methodologies	methodology	NOUN
cana-5959	57	7	(	(	PUNCT
cana-5959	57	8	rousseeuw	rousseeuw	PROPN
cana-5959	57	9	&	&	CCONJ
cana-5959	57	10	leroy	leroy	PROPN
cana-5959	57	11	,	,	PUNCT
cana-5959	57	12	1987	1987	NUM
cana-5959	57	13	;	;	PUNCT
cana-5959	57	14	müller	müller	PROPN
cana-5959	57	15	&	&	CCONJ
cana-5959	57	16	welsh	welsh	PROPN
cana-5959	57	17	,	,	PUNCT
cana-5959	57	18	2005	2005	NUM
cana-5959	57	19	)	)	PUNCT
cana-5959	57	20	,	,	PUNCT
cana-5959	57	21	the	the	DET
cana-5959	57	22	owpm	owpm	NOUN
cana-5959	57	23	alters	alter	VERB
cana-5959	57	24	the	the	DET
cana-5959	57	25	log	log	NOUN
cana-5959	57	26	-	-	PUNCT
cana-5959	57	27	likelihood	likelihood	NOUN
cana-5959	57	28	function	function	NOUN
cana-5959	57	29	by	by	ADP
cana-5959	57	30	allocating	allocate	VERB
cana-5959	57	31	reduced	reduce	VERB
cana-5959	57	32	weights	weight	NOUN
cana-5959	57	33	to	to	ADP
cana-5959	57	34	observations	observation	NOUN
cana-5959	57	35	that	that	PRON
cana-5959	57	36	display	display	VERB
cana-5959	57	37	excessive	excessive	ADJ
cana-5959	57	38	deviance	deviance	NOUN
cana-5959	57	39	residuals	residual	NOUN
cana-5959	57	40	:	:	PUNCT
cana-5959	57	41	the	the	DET
cana-5959	57	42	weights	weight	NOUN
cana-5959	57	43	are	be	AUX
cana-5959	57	44	determined	determine	VERB
cana-5959	57	45	using	use	VERB
cana-5959	57	46	robust	robust	ADJ
cana-5959	57	47	diagnostic	diagnostic	ADJ
cana-5959	57	48	metrics	metric	NOUN
cana-5959	57	49	(	(	PUNCT
cana-5959	57	50	such	such	ADJ
cana-5959	57	51	as	as	ADP
cana-5959	57	52	deviance	deviance	NOUN
cana-5959	57	53	or	or	CCONJ
cana-5959	57	54	cook	cook	VERB
cana-5959	57	55	’s	’s	PART
cana-5959	57	56	distance	distance	NOUN
cana-5959	57	57	)	)	PUNCT
cana-5959	57	58	,	,	PUNCT
cana-5959	57	59	with	with	ADP
cana-5959	57	60	thresholds	threshold	NOUN
cana-5959	57	61	established	establish	VERB
cana-5959	57	62	through	through	ADP
cana-5959	57	63	simulation	simulation	NOUN
cana-5959	57	64	or	or	CCONJ
cana-5959	57	65	crossvalidation	crossvalidation	NOUN
cana-5959	57	66	(	(	PUNCT
cana-5959	57	67	ma	ma	PROPN
cana-5959	57	68	et	et	PROPN
cana-5959	57	69	al	al	PROPN
cana-5959	57	70	.	.	PROPN
cana-5959	57	71	,	,	PUNCT
cana-5959	57	72	2017	2017	NUM
cana-5959	57	73	;	;	PUNCT
cana-5959	57	74	jin	jin	PROPN
cana-5959	57	75	et	et	PROPN
cana-5959	57	76	al	al	PROPN
cana-5959	57	77	.	.	PROPN
cana-5959	57	78	,	,	PUNCT
cana-5959	57	79	2020	2020	NUM
cana-5959	57	80	)	)	PUNCT
cana-5959	57	81	.	.	PUNCT
cana-5959	58	1	this	this	DET
cana-5959	58	2	methodology	methodology	NOUN
cana-5959	58	3	provides	provide	VERB
cana-5959	58	4	two	two	NUM
cana-5959	58	5	significant	significant	ADJ
cana-5959	58	6	advantages	advantage	NOUN
cana-5959	58	7	:	:	PUNCT
cana-5959	58	8	it	it	PRON
cana-5959	58	9	reduces	reduce	VERB
cana-5959	58	10	the	the	DET
cana-5959	58	11	impact	impact	NOUN
cana-5959	58	12	of	of	ADP
cana-5959	58	13	outliers	outlier	NOUN
cana-5959	58	14	on	on	ADP
cana-5959	58	15	parameter	parameter	NOUN
cana-5959	58	16	estimation	estimation	NOUN
cana-5959	58	17	and	and	CCONJ
cana-5959	58	18	indirectly	indirectly	ADV
cana-5959	58	19	mitigates	mitigate	VERB
cana-5959	58	20	overdispersion	overdispersion	NOUN
cana-5959	58	21	resulting	result	VERB
cana-5959	58	22	from	from	ADP
cana-5959	58	23	such	such	ADJ
cana-5959	58	24	anomalies	anomaly	NOUN
cana-5959	58	25	.	.	PUNCT
cana-5959	59	1	unlike	unlike	ADP
cana-5959	59	2	traditional	traditional	ADJ
cana-5959	59	3	poisson	poisson	NOUN
cana-5959	59	4	or	or	CCONJ
cana-5959	59	5	negative	negative	ADJ
cana-5959	59	6	binomial	binomial	ADJ
cana-5959	59	7	(	(	PUNCT
cana-5959	59	8	nb	nb	NOUN
cana-5959	59	9	)	)	PUNCT
cana-5959	59	10	models	model	NOUN
cana-5959	59	11	,	,	PUNCT
cana-5959	59	12	the	the	DET
cana-5959	59	13	owpm	owpm	NOUN
cana-5959	59	14	is	be	AUX
cana-5959	59	15	particularly	particularly	ADV
cana-5959	59	16	adept	adept	ADJ
cana-5959	59	17	at	at	ADP
cana-5959	59	18	handling	handle	VERB
cana-5959	59	19	datasets	dataset	NOUN
cana-5959	59	20	where	where	SCONJ
cana-5959	59	21	overdispersion	overdispersion	NOUN
cana-5959	59	22	is	be	AUX
cana-5959	59	23	a	a	DET
cana-5959	59	24	consequence	consequence	NOUN
cana-5959	59	25	of	of	ADP
cana-5959	59	26	a	a	DET
cana-5959	59	27	small	small	ADJ
cana-5959	59	28	number	number	NOUN
cana-5959	59	29	of	of	ADP
cana-5959	59	30	influential	influential	ADJ
cana-5959	59	31	observations	observation	NOUN
cana-5959	59	32	rather	rather	ADV
cana-5959	59	33	than	than	ADP
cana-5959	59	34	stemming	stem	VERB
cana-5959	59	35	from	from	ADP
cana-5959	59	36	a	a	DET
cana-5959	59	37	genuine	genuine	ADJ
cana-5959	59	38	latent	latent	NOUN
cana-5959	59	39	variance	variance	NOUN
cana-5959	59	40	structure	structure	NOUN
cana-5959	59	41	.	.	PUNCT
cana-5959	60	1	consequently	consequently	ADV
cana-5959	60	2	,	,	PUNCT
cana-5959	60	3	this	this	DET
cana-5959	60	4	model	model	NOUN
cana-5959	60	5	presents	present	VERB
cana-5959	60	6	a	a	DET
cana-5959	60	7	novel	novel	ADJ
cana-5959	60	8	and	and	CCONJ
cana-5959	60	9	interpretable	interpretable	ADJ
cana-5959	60	10	alternative	alternative	NOUN
cana-5959	60	11	to	to	ADP
cana-5959	60	12	established	establish	VERB
cana-5959	60	13	techniques	technique	NOUN
cana-5959	60	14	.	.	PUNCT
cana-5959	61	1	3.3	3.3	NUM
cana-5959	61	2	negative	negative	ADJ
cana-5959	61	3	binomial	binomial	ADJ
cana-5959	61	4	regression	regression	NOUN
cana-5959	61	5	(	(	PUNCT
cana-5959	61	6	nb	nb	INTJ
cana-5959	61	7	)	)	PUNCT
cana-5959	61	8	the	the	DET
cana-5959	61	9	negative	negative	ADJ
cana-5959	61	10	binomial	binomial	NOUN
cana-5959	61	11	(	(	PUNCT
cana-5959	61	12	nb	nb	NOUN
cana-5959	61	13	)	)	PUNCT
cana-5959	61	14	model	model	NOUN
cana-5959	61	15	extends	extend	VERB
cana-5959	61	16	the	the	DET
cana-5959	61	17	poisson	poisson	NOUN
cana-5959	61	18	distribution	distribution	NOUN
cana-5959	61	19	by	by	ADP
cana-5959	61	20	incorporating	incorporate	VERB
cana-5959	61	21	a	a	DET
cana-5959	61	22	specific	specific	ADJ
cana-5959	61	23	overdispersion	overdispersion	NOUN
cana-5959	61	24	parameter	parameter	NOUN
cana-5959	61	25	θ	θ	PROPN
cana-5959	61	26	,	,	PUNCT
cana-5959	61	27	thereby	thereby	ADV
cana-5959	61	28	accommodating	accommodate	VERB
cana-5959	61	29	extra	extra	ADJ
cana-5959	61	30	-	-	ADJ
cana-5959	61	31	poisson	poisson	ADJ
cana-5959	61	32	variation	variation	NOUN
cana-5959	61	33	:	:	PUNCT
cana-5959	61	34	the	the	DET
cana-5959	61	35	nb	nb	PROPN
cana-5959	61	36	model	model	NOUN
cana-5959	61	37	posits	posit	NOUN
cana-5959	61	38	that	that	SCONJ
cana-5959	61	39	the	the	DET
cana-5959	61	40	count	count	NOUN
cana-5959	61	41	variable	variable	NOUN
cana-5959	61	42	is	be	AUX
cana-5959	61	43	derived	derive	VERB
cana-5959	61	44	from	from	ADP
cana-5959	61	45	a	a	DET
cana-5959	61	46	poisson	poisson	NOUN
cana-5959	61	47	-	-	PUNCT
cana-5959	61	48	gamma	gamma	NOUN
cana-5959	61	49	mixture	mixture	NOUN
cana-5959	61	50	,	,	PUNCT
cana-5959	61	51	where	where	SCONJ
cana-5959	61	52	the	the	DET
cana-5959	61	53	mean	mean	NOUN
cana-5959	61	54	follows	follow	VERB
cana-5959	61	55	a	a	DET
cana-5959	61	56	gamma	gamma	NOUN
cana-5959	61	57	distribution	distribution	NOUN
cana-5959	61	58	to	to	PART
cana-5959	61	59	account	account	VERB
cana-5959	61	60	for	for	ADP
cana-5959	61	61	unobserved	unobserved	ADJ
cana-5959	61	62	heterogeneity	heterogeneity	NOUN
cana-5959	61	63	(	(	PUNCT
cana-5959	61	64	lawless	lawless	ADJ
cana-5959	61	65	,	,	PUNCT
cana-5959	61	66	1987	1987	NUM
cana-5959	61	67	;	;	PUNCT
cana-5959	61	68	hilbe	hilbe	NOUN
cana-5959	61	69	,	,	PUNCT
cana-5959	61	70	2014	2014	NUM
cana-5959	61	71	)	)	PUNCT
cana-5959	61	72	.	.	PUNCT
cana-5959	62	1	estimation	estimation	NOUN
cana-5959	62	2	is	be	AUX
cana-5959	62	3	generally	generally	ADV
cana-5959	62	4	performed	perform	VERB
cana-5959	62	5	using	use	VERB
cana-5959	62	6	maximum	maximum	ADJ
cana-5959	62	7	likelihood	likelihood	NOUN
cana-5959	62	8	,	,	PUNCT
cana-5959	62	9	with	with	ADP
cana-5959	62	10	the	the	DET
cana-5959	62	11	overdispersion	overdispersion	NOUN
cana-5959	62	12	parameter	parameter	NOUN
cana-5959	62	13	θ	θ	PROPN
cana-5959	62	14	estimated	estimate	VERB
cana-5959	62	15	concurrently	concurrently	ADV
cana-5959	62	16	.	.	PUNCT
cana-5959	63	1	while	while	SCONJ
cana-5959	63	2	effective	effective	ADJ
cana-5959	63	3	in	in	ADP
cana-5959	63	4	numerous	numerous	ADJ
cana-5959	63	5	practical	practical	ADJ
cana-5959	63	6	scenarios	scenario	NOUN
cana-5959	63	7	,	,	PUNCT
cana-5959	63	8	nb	nb	PROPN
cana-5959	63	9	models	model	NOUN
cana-5959	63	10	presuppose	presuppose	VERB
cana-5959	63	11	a	a	DET
cana-5959	63	12	specific	specific	ADJ
cana-5959	63	13	form	form	NOUN
cana-5959	63	14	of	of	ADP
cana-5959	63	15	overdispersion	overdispersion	NOUN
cana-5959	63	16	that	that	PRON
cana-5959	63	17	may	may	AUX
cana-5959	63	18	not	not	PART
cana-5959	63	19	be	be	AUX
cana-5959	63	20	ideal	ideal	ADJ
cana-5959	63	21	when	when	SCONJ
cana-5959	63	22	the	the	DET
cana-5959	63	23	excess	excess	ADJ
cana-5959	63	24	variance	variance	NOUN
cana-5959	63	25	is	be	AUX
cana-5959	63	26	attributable	attributable	ADJ
cana-5959	63	27	to	to	ADP
cana-5959	63	28	isolated	isolate	VERB
cana-5959	63	29	outliers	outlier	NOUN
cana-5959	63	30	rather	rather	ADV
cana-5959	63	31	than	than	ADP
cana-5959	63	32	a	a	DET
cana-5959	63	33	broad	broad	ADJ
cana-5959	63	34	distributional	distributional	ADJ
cana-5959	63	35	spread	spread	NOUN
cana-5959	63	36	.	.	PUNCT
cana-5959	64	1	furthermore	furthermore	ADV
cana-5959	64	2	,	,	PUNCT
cana-5959	64	3	the	the	DET
cana-5959	64	4	nb	nb	PROPN
cana-5959	64	5	model	model	NOUN
cana-5959	64	6	does	do	AUX
cana-5959	64	7	not	not	PART
cana-5959	64	8	down	down	ADP
cana-5959	64	9	-	-	PUNCT
cana-5959	64	10	weight	weight	NOUN
cana-5959	64	11	individual	individual	ADJ
cana-5959	64	12	data	datum	NOUN
cana-5959	64	13	points	point	NOUN
cana-5959	64	14	and	and	CCONJ
cana-5959	64	15	may	may	AUX
cana-5959	64	16	remain	remain	VERB
cana-5959	64	17	susceptible	susceptible	ADJ
cana-5959	64	18	to	to	ADP
cana-5959	64	19	leverage	leverage	NOUN
cana-5959	64	20	effects	effect	NOUN
cana-5959	64	21	(	(	PUNCT
cana-5959	64	22	ver	ver	PROPN
cana-5959	64	23	hoef&boveng	hoef&boveng	PROPN
cana-5959	64	24	,	,	PUNCT
cana-5959	64	25	2007	2007	NUM
cana-5959	64	26	)	)	PUNCT
cana-5959	64	27	.	.	PUNCT
cana-5959	65	1	communications	communication	NOUN
cana-5959	65	2	on	on	ADP
cana-5959	65	3	applied	apply	VERB
cana-5959	65	4	nonlinear	nonlinear	ADJ
cana-5959	65	5	analysis	analysis	NOUN
cana-5959	65	6	issn	issn	NOUN
cana-5959	65	7	:	:	PUNCT
cana-5959	65	8	1074	1074	NUM
cana-5959	65	9	-	-	PUNCT
cana-5959	65	10	133x	133x	NUM
cana-5959	65	11	vol	vol	VERB
cana-5959	65	12	32	32	NUM
cana-5959	65	13	no	no	NOUN
cana-5959	65	14	.	.	PUNCT
cana-5959	66	1	10s	10	NOUN
cana-5959	66	2	(	(	PUNCT
cana-5959	66	3	2025	2025	NUM
cana-5959	66	4	)	)	PUNCT
cana-5959	66	5	3195	3195	NUM
cana-5959	66	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	66	7	summary	summary	NOUN
cana-5959	66	8	model	model	NOUN
cana-5959	66	9	handles	handle	VERB
cana-5959	66	10	overdispersion	overdispersion	PROPN
cana-5959	66	11	?	?	PUNCT
cana-5959	67	1	handles	handle	VERB
cana-5959	67	2	outliers	outlier	NOUN
cana-5959	67	3	?	?	PUNCT
cana-5959	68	1	approach	approach	NOUN
cana-5959	68	2	standard	standard	ADJ
cana-5959	68	3	poisson	poisson	NOUN
cana-5959	68	4	(	(	PUNCT
cana-5959	68	5	sp	sp	NOUN
cana-5959	68	6	)	)	PUNCT
cana-5959	68	7	✗	✗	PROPN
cana-5959	68	8	✗	✗	PROPN
cana-5959	68	9	assumes	assume	VERB
cana-5959	68	10	equidispersion	equidispersion	NOUN
cana-5959	68	11	owpm	owpm	NOUN
cana-5959	68	12	(	(	PUNCT
cana-5959	68	13	proposed	propose	VERB
cana-5959	68	14	)	)	PUNCT
cana-5959	68	15	✔	✔	NOUN
cana-5959	68	16	✔	✔	NOUN
cana-5959	68	17	robust	robust	ADJ
cana-5959	68	18	weighted	weight	VERB
cana-5959	68	19	likelihood	likelihood	NOUN
cana-5959	68	20	negative	negative	ADJ
cana-5959	68	21	binomial	binomial	ADJ
cana-5959	68	22	(	(	PUNCT
cana-5959	68	23	nb	nb	NOUN
cana-5959	68	24	)	)	PUNCT
cana-5959	68	25	✔	✔	PROPN
cana-5959	68	26	✗	✗	PROPN
cana-5959	68	27	parametric	parametric	PROPN
cana-5959	68	28	overdispersion	overdispersion	PROPN
cana-5959	68	29	2.5	2.5	NUM
cana-5959	68	30	evaluation	evaluation	NOUN
cana-5959	68	31	metrics	metric	NOUN
cana-5959	68	32	•prediction	•prediction	NOUN
cana-5959	68	33	accuracy	accuracy	NOUN
cana-5959	68	34	:	:	PUNCT
cana-5959	68	35	mean	mean	VERB
cana-5959	68	36	squared	square	VERB
cana-5959	68	37	error	error	NOUN
cana-5959	68	38	(	(	PUNCT
cana-5959	68	39	mse	mse	NOUN
cana-5959	68	40	)	)	PUNCT
cana-5959	68	41	,	,	PUNCT
cana-5959	68	42	mean	mean	VERB
cana-5959	68	43	absolute	absolute	ADJ
cana-5959	68	44	error	error	NOUN
cana-5959	68	45	(	(	PUNCT
cana-5959	68	46	mae	mae	PROPN
cana-5959	68	47	)	)	PUNCT
cana-5959	68	48	.	.	PUNCT
cana-5959	69	1	•model	•model	PROPN
cana-5959	69	2	fit	fit	NOUN
cana-5959	69	3	:	:	PUNCT
cana-5959	69	4	akaike	akaike	ADJ
cana-5959	69	5	information	information	NOUN
cana-5959	69	6	criterion	criterion	NOUN
cana-5959	69	7	(	(	PUNCT
cana-5959	69	8	aic	aic	PROPN
cana-5959	69	9	)	)	PUNCT
cana-5959	69	10	,	,	PUNCT
cana-5959	69	11	bayesian	bayesian	NOUN
cana-5959	69	12	information	information	NOUN
cana-5959	69	13	criterion	criterion	NOUN
cana-5959	69	14	(	(	PUNCT
cana-5959	69	15	bic	bic	PROPN
cana-5959	69	16	)	)	PUNCT
cana-5959	69	17	.	.	PUNCT
cana-5959	70	1	•dispersion	•dispersion	NOUN
cana-5959	70	2	parameter	parameter	NOUN
cana-5959	70	3	.	.	PUNCT
cana-5959	71	1	•pseudo(mcfadden	•pseudo(mcfadden	PROPN
cana-5959	71	2	’s	’s	PART
cana-5959	71	3	)	)	PUNCT
cana-5959	71	4	.	.	PUNCT
cana-5959	72	1	2.6	2.6	NUM
cana-5959	72	2	statistical	statistical	ADJ
cana-5959	72	3	testing	testing	NOUN
cana-5959	72	4	and	and	CCONJ
cana-5959	72	5	graphical	graphical	ADJ
cana-5959	72	6	diagnostics	diagnostic	NOUN
cana-5959	72	7	•wilcoxon	•wilcoxon	PROPN
cana-5959	72	8	signed	sign	VERB
cana-5959	72	9	-	-	PUNCT
cana-5959	72	10	rank	rank	NOUN
cana-5959	72	11	tests	test	NOUN
cana-5959	72	12	compare	compare	VERB
cana-5959	72	13	paired	pair	VERB
cana-5959	72	14	residual	residual	ADJ
cana-5959	72	15	errors	error	NOUN
cana-5959	72	16	between	between	ADP
cana-5959	72	17	models	model	NOUN
cana-5959	72	18	.	.	PUNCT
cana-5959	73	1	•residual	•residual	ADJ
cana-5959	73	2	boxplots	boxplot	NOUN
cana-5959	73	3	and	and	CCONJ
cana-5959	73	4	predicted	predict	VERB
cana-5959	73	5	-	-	PUNCT
cana-5959	73	6	vs	vs	ADP
cana-5959	73	7	-	-	PUNCT
cana-5959	73	8	observed	observe	VERB
cana-5959	73	9	plots	plot	NOUN
cana-5959	73	10	assess	assess	VERB
cana-5959	73	11	model	model	NOUN
cana-5959	73	12	fit	fit	ADJ
cana-5959	73	13	and	and	CCONJ
cana-5959	73	14	residual	residual	ADJ
cana-5959	73	15	behavior	behavior	NOUN
cana-5959	73	16	.	.	PUNCT
cana-5959	74	1	3	3	X
cana-5959	74	2	.	.	NOUN
cana-5959	74	3	results	result	VERB
cana-5959	74	4	3.1	3.1	NUM
cana-5959	74	5	.	.	PUNCT
cana-5959	75	1	numerical	numerical	PROPN
cana-5959	75	2	results	result	NOUN
cana-5959	75	3	table	table	NOUN
cana-5959	75	4	1	1	NUM
cana-5959	75	5	summarizes	summarize	NOUN
cana-5959	75	6	the	the	DET
cana-5959	75	7	average	average	ADJ
cana-5959	75	8	metrics	metric	NOUN
cana-5959	75	9	across	across	ADP
cana-5959	75	10	simulations	simulation	NOUN
cana-5959	75	11	for	for	ADP
cana-5959	75	12	varying	vary	VERB
cana-5959	75	13	outlier	outlier	NOUN
cana-5959	75	14	percentages	percentage	NOUN
cana-5959	75	15	(	(	PUNCT
cana-5959	75	16	5	5	NUM
cana-5959	75	17	%	%	NOUN
cana-5959	75	18	,	,	PUNCT
cana-5959	75	19	10	10	NUM
cana-5959	75	20	%	%	NOUN
cana-5959	75	21	,	,	PUNCT
cana-5959	75	22	20	20	NUM
cana-5959	75	23	%	%	NOUN
cana-5959	75	24	)	)	PUNCT
cana-5959	75	25	and	and	CCONJ
cana-5959	75	26	regression	regression	NOUN
cana-5959	75	27	types	type	NOUN
cana-5959	75	28	.	.	PUNCT
cana-5959	76	1	model	model	NOUN
cana-5959	76	2	%	%	NOUN
cana-5959	77	1	r	r	NOUN
cana-5959	77	2	.typ	.typ	NOUN
cana-5959	77	3	e	e	PROPN
cana-5959	77	4	mse	mse	PROPN
cana-5959	77	5	mae	mae	PROPN
cana-5959	77	6	aic	aic	PROPN
cana-5959	77	7	bic	bic	PROPN
cana-5959	77	8	dispersio	dispersio	VERB
cana-5959	77	9	n	n	CCONJ
cana-5959	77	10	pseudo	pseudo	NOUN
cana-5959	77	11	-	-	ADJ
cana-5959	77	12	r²	r²	ADJ
cana-5959	77	13	poisson	poisson	NOUN
cana-5959	77	14	5	5	NUM
cana-5959	77	15	simple	simple	ADJ
cana-5959	77	16	14.0348	14.0348	NUM
cana-5959	77	17	2.31974	2.31974	NUM
cana-5959	77	18	2747.1477	2747.1477	NUM
cana-5959	77	19	2755.57	2755.57	NUM
cana-5959	77	20	3.5752	3.5752	NUM
cana-5959	77	21	0.24882339	0.24882339	NUM
cana-5959	77	22	owpm	owpm	NOUN
cana-5959	77	23	5	5	NUM
cana-5959	77	24	simple	simple	ADJ
cana-5959	77	25	13.9070	13.9070	NUM
cana-5959	77	26	2.280604	2.280604	NUM
cana-5959	77	27	1117.6453	1117.6453	NUM
cana-5959	77	28	1126.07	1126.07	NUM
cana-5959	77	29	0.4597	0.4597	NUM
cana-5959	77	30	0.69504218	0.69504218	NUM
cana-5959	77	31	negbin	negbin	PROPN
cana-5959	77	32	5	5	NUM
cana-5959	77	33	simple	simple	ADJ
cana-5959	77	34	14.5995	14.5995	NUM
cana-5959	77	35	2.372960	2.372960	NUM
cana-5959	77	36	2389.0933	2389.0933	ADP
cana-5959	77	37	2401.73	2401.73	NUM
cana-5959	77	38	1.3754	1.3754	NUM
cana-5959	77	39	0.34741977	0.34741977	NUM
cana-5959	77	40	poisson	poisson	NOUN
cana-5959	77	41	10	10	NUM
cana-5959	77	42	simple	simple	ADJ
cana-5959	77	43	24.1222	24.1222	NUM
cana-5959	77	44	2.771021	2.771021	NUM
cana-5959	77	45	3223.2697	3223.2697	NUM
cana-5959	77	46	3231.69	3231.69	NUM
cana-5959	77	47	5.3538	5.3538	NUM
cana-5959	77	48	0.24463759	0.24463759	NUM
cana-5959	77	49	owpm	owpm	VERB
cana-5959	77	50	10	10	NUM
cana-5959	77	51	simple	simple	ADJ
cana-5959	77	52	24.1545	24.1545	NUM
cana-5959	77	53	2.690653	2.690653	NUM
cana-5959	77	54	1124.5345	1124.5345	NUM
cana-5959	77	55	1132.96	1132.96	NUM
cana-5959	77	56	0.4286	0.4286	NUM
cana-5959	77	57	0.73708023	0.73708023	NUM
cana-5959	77	58	negbin	negbin	PROPN
cana-5959	77	59	10	10	NUM
cana-5959	77	60	simple	simple	ADJ
cana-5959	77	61	25.5860	25.5860	NUM
cana-5959	77	62	2.843701	2.843701	NUM
cana-5959	77	63	2549.0598	2549.0598	NUM
cana-5959	77	64	2561.70	2561.70	NUM
cana-5959	77	65	1.5857	1.5857	NUM
cana-5959	77	66	0.40330200	0.40330200	NUM
cana-5959	77	67	communications	communication	NOUN
cana-5959	77	68	on	on	ADP
cana-5959	77	69	applied	apply	VERB
cana-5959	77	70	nonlinear	nonlinear	ADJ
cana-5959	77	71	analysis	analysis	NOUN
cana-5959	77	72	issn	issn	NOUN
cana-5959	77	73	:	:	PUNCT
cana-5959	77	74	1074	1074	NUM
cana-5959	77	75	-	-	PUNCT
cana-5959	77	76	133x	133x	NUM
cana-5959	77	77	vol	vol	VERB
cana-5959	77	78	32	32	NUM
cana-5959	77	79	no	no	NOUN
cana-5959	77	80	.	.	PUNCT
cana-5959	78	1	10s	10	NOUN
cana-5959	78	2	(	(	PUNCT
cana-5959	78	3	2025	2025	NUM
cana-5959	78	4	)	)	PUNCT
cana-5959	78	5	3196	3196	NUM
cana-5959	78	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	78	7	poisson	poisson	NOUN
cana-5959	78	8	20	20	NUM
cana-5959	78	9	simple	simple	ADJ
cana-5959	78	10	43.0706	43.0706	NUM
cana-5959	79	1	3.822135	3.822135	NUM
cana-5959	79	2	915.2047	915.2047	NUM
cana-5959	79	3	923.633	923.633	NUM
cana-5959	79	4	0.3489	0.3489	NUM
cana-5959	79	5	0.09725199	0.09725199	NUM
cana-5959	79	6	owpm	owpm	NOUN
cana-5959	79	7	20	20	NUM
cana-5959	79	8	simple	simple	ADJ
cana-5959	79	9	43.0706	43.0706	NUM
cana-5959	80	1	3.822135	3.822135	NUM
cana-5959	80	2	915.2047	915.2047	NUM
cana-5959	80	3	923.633	923.633	NUM
cana-5959	80	4	0.3489	0.3489	NUM
cana-5959	80	5	0.81402525	0.81402525	NUM
cana-5959	80	6	negbin	negbin	PROPN
cana-5959	80	7	20	20	NUM
cana-5959	80	8	simple	simple	ADJ
cana-5959	80	9	42.9949	42.9949	NUM
cana-5959	80	10	4.210295	4.210295	NUM
cana-5959	80	11	2737.0325	2737.0325	NUM
cana-5959	80	12	2749.67	2749.67	NUM
cana-5959	80	13	1.2962	1.2962	NUM
cana-5959	80	14	0.44260263	0.44260263	NUM
cana-5959	80	15	poisson	poisson	NOUN
cana-5959	80	16	5	5	NUM
cana-5959	80	17	multipl	multipl	NOUN
cana-5959	80	18	e	e	NOUN
cana-5959	80	19	15.9955	15.9955	NUM
cana-5959	80	20	2.456474	2.456474	NUM
cana-5959	80	21	2906.8426	2906.8426	NUM
cana-5959	80	22	2919.48	2919.48	NUM
cana-5959	80	23	4.0047	4.0047	NUM
cana-5959	80	24	0.09256853	0.09256853	NUM
cana-5959	80	25	owpm	owpm	NOUN
cana-5959	80	26	5	5	NUM
cana-5959	80	27	multipl	multipl	NOUN
cana-5959	80	28	e	e	NOUN
cana-5959	80	29	16.0731	16.0731	NUM
cana-5959	80	30	2.420899	2.420899	NUM
cana-5959	80	31	1074.7871	1074.7871	NUM
cana-5959	80	32	1087.43	1087.43	NUM
cana-5959	80	33	0.4563	0.4563	NUM
cana-5959	80	34	0.66566576	0.66566576	NUM
cana-5959	80	35	negbin	negbin	NOUN
cana-5959	80	36	5	5	NUM
cana-5959	80	37	multipl	multipl	NOUN
cana-5959	80	38	e	e	NOUN
cana-5959	80	39	16.0279	16.0279	NUM
cana-5959	80	40	2.465953	2.465953	NUM
cana-5959	80	41	2402.2711	2402.2711	NOUN
cana-5959	80	42	2419.12	2419.12	NUM
cana-5959	80	43	1.3011	1.3011	NUM
cana-5959	80	44	0.25103248	0.25103248	NUM
cana-5959	80	45	poisson	poisson	NOUN
cana-5959	80	46	10	10	NUM
cana-5959	80	47	multipl	multipl	NOUN
cana-5959	80	48	e	e	NOUN
cana-5959	80	49	28.1461	28.1461	NUM
cana-5959	80	50	3.110467	3.110467	NUM
cana-5959	80	51	3670.7887	3670.7887	NUM
cana-5959	80	52	3683.43	3683.43	NUM
cana-5959	80	53	6.8603	6.8603	NUM
cana-5959	80	54	0.04295925	0.04295925	NUM
cana-5959	80	55	owpm	owpm	NOUN
cana-5959	80	56	10	10	NUM
cana-5959	80	57	multipl	multipl	NOUN
cana-5959	80	58	e	e	X
cana-5959	80	59	28.3315	28.3315	NUM
cana-5959	80	60	2.968628	2.968628	NUM
cana-5959	80	61	1065.8775	1065.8775	NUM
cana-5959	80	62	1078.52	1078.52	NUM
cana-5959	80	63	0.4161	0.4161	NUM
cana-5959	80	64	0.72321845	0.72321845	NUM
cana-5959	80	65	negbin	negbin	PROPN
cana-5959	80	66	10	10	NUM
cana-5959	80	67	multipl	multipl	NOUN
cana-5959	80	68	e	e	NOUN
cana-5959	80	69	28.3508	28.3508	NUM
cana-5959	80	70	3.142403	3.142403	NUM
cana-5959	80	71	2594.4792	2594.4792	NUM
cana-5959	80	72	2611.33	2611.33	NUM
cana-5959	80	73	1.5381	1.5381	NUM
cana-5959	80	74	0.32455424	0.32455424	NUM
cana-5959	80	75	poisson	poisson	NOUN
cana-5959	80	76	20	20	NUM
cana-5959	80	77	multipl	multipl	NOUN
cana-5959	80	78	e	e	NOUN
cana-5959	80	79	37.6573	37.6573	NUM
cana-5959	80	80	3.877778	3.877778	NUM
cana-5959	80	81	4294.1828	4294.1828	PROPN
cana-5959	80	82	4306.82	4306.82	NUM
cana-5959	80	83	8.5491	8.5491	NUM
cana-5959	80	84	0.03757781	0.03757781	NUM
cana-5959	80	85	owpm	owpm	NOUN
cana-5959	80	86	20	20	NUM
cana-5959	80	87	multipl	multipl	NOUN
cana-5959	80	88	e	e	X
cana-5959	80	89	38.2890	38.2890	NUM
cana-5959	80	90	3.598852	3.598852	NUM
cana-5959	80	91	950.9113	950.9113	NUM
cana-5959	80	92	963.555	963.555	NUM
cana-5959	80	93	0.4275	0.4275	NUM
cana-5959	80	94	0.78792798	0.78792798	NUM
cana-5959	80	95	negbin	negbin	PROPN
cana-5959	80	96	20	20	NUM
cana-5959	80	97	multipl	multipl	NOUN
cana-5959	80	98	e	e	NOUN
cana-5959	80	99	37.9572	37.9572	NUM
cana-5959	80	100	3.924511	3.924511	NUM
cana-5959	80	101	2676.0596	2676.0596	NUM
cana-5959	80	102	2692.91	2692.91	NUM
cana-5959	80	103	1.3525	1.3525	NUM
cana-5959	80	104	0.40119163	0.40119163	NUM
cana-5959	80	105	1	1	NUM
cana-5959	80	106	.	.	PUNCT
cana-5959	81	1	the	the	DET
cana-5959	81	2	mean	mean	ADJ
cana-5959	81	3	squared	square	VERB
cana-5959	81	4	error	error	NOUN
cana-5959	81	5	(	(	PUNCT
cana-5959	81	6	mse	mse	NOUN
cana-5959	81	7	)	)	PUNCT
cana-5959	81	8	and	and	CCONJ
cana-5959	81	9	mean	mean	VERB
cana-5959	81	10	absolute	absolute	ADJ
cana-5959	81	11	error	error	NOUN
cana-5959	81	12	(	(	PUNCT
cana-5959	81	13	mae	mae	PROPN
cana-5959	81	14	)	)	PUNCT
cana-5959	81	15	exhibit	exhibit	VERB
cana-5959	81	16	a	a	DET
cana-5959	81	17	significant	significant	ADJ
cana-5959	81	18	increase	increase	NOUN
cana-5959	81	19	as	as	ADP
cana-5959	81	20	the	the	DET
cana-5959	81	21	percentage	percentage	NOUN
cana-5959	81	22	of	of	ADP
cana-5959	81	23	outliers	outlier	NOUN
cana-5959	81	24	in	in	ADP
cana-5959	81	25	the	the	DET
cana-5959	81	26	sp	sp	NOUN
cana-5959	81	27	rises	rise	NOUN
cana-5959	81	28	,	,	PUNCT
cana-5959	81	29	while	while	SCONJ
cana-5959	81	30	the	the	DET
cana-5959	81	31	outlier	outlier	NOUN
cana-5959	81	32	weighted	weight	VERB
cana-5959	81	33	prediction	prediction	NOUN
cana-5959	81	34	model	model	NOUN
cana-5959	81	35	(	(	PUNCT
cana-5959	81	36	owpm	owpm	PROPN
cana-5959	81	37	)	)	PUNCT
cana-5959	81	38	and	and	CCONJ
cana-5959	81	39	naive	naive	ADJ
cana-5959	81	40	bayes	bayes	NOUN
cana-5959	81	41	(	(	PUNCT
cana-5959	81	42	nb	nb	NOUN
cana-5959	81	43	)	)	PUNCT
cana-5959	81	44	demonstrate	demonstrate	VERB
cana-5959	81	45	greater	great	ADJ
cana-5959	81	46	stability	stability	NOUN
cana-5959	81	47	and	and	CCONJ
cana-5959	81	48	lower	low	ADJ
cana-5959	81	49	values	value	NOUN
cana-5959	81	50	.	.	PUNCT
cana-5959	82	1	2	2	X
cana-5959	82	2	.	.	X
cana-5959	82	3	the	the	DET
cana-5959	82	4	dispersion	dispersion	NOUN
cana-5959	82	5	in	in	ADP
cana-5959	82	6	sp	sp	ADP
cana-5959	82	7	surpasses	surpasse	NOUN
cana-5959	82	8	the	the	DET
cana-5959	82	9	threshold	threshold	NOUN
cana-5959	82	10	of	of	ADP
cana-5959	82	11	1	1	NUM
cana-5959	82	12	,	,	PUNCT
cana-5959	82	13	indicating	indicate	VERB
cana-5959	82	14	a	a	DET
cana-5959	82	15	state	state	NOUN
cana-5959	82	16	of	of	ADP
cana-5959	82	17	overdispersion	overdispersion	NOUN
cana-5959	82	18	;	;	PUNCT
cana-5959	82	19	in	in	ADP
cana-5959	82	20	c	c	NOUN
cana-5959	82	21	o	o	NOUN
cana-5959	82	22	n	n	ADP
cana-5959	82	23	t	t	NOUN
cana-5959	82	24	r	r	NOUN
cana-5959	82	25	a	a	DET
cana-5959	82	26	s	s	PROPN
cana-5959	82	27	t	t	NOUN
cana-5959	82	28	,	,	PUNCT
cana-5959	82	29	o	o	PROPN
cana-5959	82	30	w	w	NOUN
cana-5959	83	1	p	p	X
cana-5959	83	2	m	m	VERB
cana-5959	83	3	a	a	DET
cana-5959	83	4	n	n	NOUN
cana-5959	83	5	d	d	PROPN
cana-5959	83	6	n	n	PROPN
cana-5959	83	7	b	b	NOUN
cana-5959	83	8	m	m	VERB
cana-5959	83	9	a	a	PRON
cana-5959	84	1	i	i	PRON
cana-5959	84	2	n	n	VERB
cana-5959	84	3	t	t	PROPN
cana-5959	85	1	a	a	PRON
cana-5959	86	1	i	i	PRON
cana-5959	87	1	n	n	PROPN
cana-5959	87	2	v	v	ADP
cana-5959	87	3	a	a	DET
cana-5959	87	4	l	l	NOUN
cana-5959	87	5	u	u	NOUN
cana-5959	87	6	e	e	PROPN
cana-5959	87	7	s	s	PROPN
cana-5959	87	8	t	t	PROPN
cana-5959	87	9	h	h	NOUN
cana-5959	87	10	a	a	DET
cana-5959	87	11	t	t	NOUN
cana-5959	87	12	a	a	DET
cana-5959	87	13	r	r	NOUN
cana-5959	87	14	e	e	NOUN
cana-5959	87	15	c	c	NOUN
cana-5959	87	16	l	l	NOUN
cana-5959	87	17	o	o	X
cana-5959	87	18	s	s	X
cana-5959	87	19	e	e	NOUN
cana-5959	87	20	r	r	NOUN
cana-5959	87	21	t	t	NOUN
cana-5959	87	22	o	o	NOUN
cana-5959	87	23	1	1	NUM
cana-5959	87	24	.	.	PUNCT
cana-5959	88	1	3	3	X
cana-5959	88	2	.	.	X
cana-5959	89	1	the	the	DET
cana-5959	89	2	akaike	akaike	ADJ
cana-5959	89	3	information	information	NOUN
cana-5959	89	4	criterion	criterion	NOUN
cana-5959	89	5	(	(	PUNCT
cana-5959	89	6	aic	aic	PROPN
cana-5959	89	7	)	)	PUNCT
cana-5959	89	8	and	and	CCONJ
cana-5959	89	9	bayesian	bayesian	NOUN
cana-5959	89	10	information	information	NOUN
cana-5959	89	11	criterion	criterion	NOUN
cana-5959	89	12	(	(	PUNCT
cana-5959	89	13	bic	bic	PROPN
cana-5959	89	14	)	)	PUNCT
cana-5959	89	15	for	for	ADP
cana-5959	89	16	owpm	owpm	NOUN
cana-5959	89	17	suggest	suggest	VERB
cana-5959	89	18	a	a	DET
cana-5959	89	19	superior	superior	ADJ
cana-5959	89	20	fit	fit	NOUN
cana-5959	89	21	compared	compare	VERB
cana-5959	89	22	to	to	ADP
cana-5959	89	23	sp	sp	VERB
cana-5959	89	24	and	and	CCONJ
cana-5959	89	25	show	show	VERB
cana-5959	89	26	competitiveness	competitiveness	NOUN
cana-5959	89	27	with	with	ADP
cana-5959	89	28	nb	nb	PROPN
cana-5959	89	29	.	.	PROPN
cana-5959	89	30	4	4	NUM
cana-5959	89	31	.	.	X
cana-5959	90	1	the	the	DET
cana-5959	90	2	pseudo	pseudo	NOUN
cana-5959	90	3	-	-	PUNCT
cana-5959	90	4	r^2	r^2	NOUN
cana-5959	90	5	values	value	NOUN
cana-5959	90	6	for	for	ADP
cana-5959	90	7	owpm	owpm	NOUN
cana-5959	90	8	and	and	CCONJ
cana-5959	90	9	nb	nb	PROPN
cana-5959	90	10	reflect	reflect	VERB
cana-5959	90	11	an	an	DET
cana-5959	90	12	enhancement	enhancement	NOUN
cana-5959	90	13	,	,	PUNCT
cana-5959	90	14	signifying	signify	VERB
cana-5959	90	15	an	an	DET
cana-5959	90	16	increase	increase	NOUN
cana-5959	90	17	in	in	ADP
cana-5959	90	18	explanatory	explanatory	ADJ
cana-5959	90	19	power	power	NOUN
cana-5959	90	20	.	.	PUNCT
cana-5959	91	1	3.2	3.2	NUM
cana-5959	91	2	graphical	graphical	ADJ
cana-5959	91	3	diagnostics	diagnostic	NOUN
cana-5959	91	4	⚫	⚫	VERB
cana-5959	91	5	the	the	DET
cana-5959	91	6	residual	residual	ADJ
cana-5959	91	7	boxplots	boxplot	NOUN
cana-5959	91	8	illustrated	illustrate	VERB
cana-5959	91	9	in	in	ADP
cana-5959	91	10	figure	figure	NOUN
cana-5959	91	11	1	1	NUM
cana-5959	91	12	reveal	reveal	VERB
cana-5959	91	13	that	that	SCONJ
cana-5959	91	14	the	the	DET
cana-5959	91	15	residuals	residual	NOUN
cana-5959	91	16	of	of	ADP
cana-5959	91	17	sp	sp	ADP
cana-5959	91	18	exhibit	exhibit	NOUN
cana-5959	91	19	a	a	DET
cana-5959	91	20	broader	broad	ADJ
cana-5959	91	21	spread	spread	NOUN
cana-5959	91	22	and	and	CCONJ
cana-5959	91	23	heavier	heavy	ADJ
cana-5959	91	24	tails	tail	NOUN
cana-5959	91	25	as	as	ADP
cana-5959	91	26	the	the	DET
cana-5959	91	27	number	number	NOUN
cana-5959	91	28	of	of	ADP
cana-5959	91	29	outliers	outlier	NOUN
cana-5959	91	30	increases	increase	NOUN
cana-5959	91	31	.	.	PUNCT
cana-5959	92	1	conversely	conversely	ADV
cana-5959	92	2	,	,	PUNCT
cana-5959	92	3	the	the	DET
cana-5959	92	4	residuals	residual	NOUN
cana-5959	92	5	for	for	ADP
cana-5959	92	6	owpm	owpm	NOUN
cana-5959	92	7	and	and	CCONJ
cana-5959	92	8	nb	nb	NOUN
cana-5959	92	9	are	be	AUX
cana-5959	92	10	more	more	ADV
cana-5959	92	11	tightly	tightly	ADV
cana-5959	92	12	clustered	clustered	ADJ
cana-5959	92	13	around	around	ADP
cana-5959	92	14	zero	zero	NUM
cana-5959	92	15	.	.	PUNCT
cana-5959	93	1	communications	communication	NOUN
cana-5959	93	2	on	on	ADP
cana-5959	93	3	applied	apply	VERB
cana-5959	93	4	nonlinear	nonlinear	ADJ
cana-5959	93	5	analysis	analysis	NOUN
cana-5959	93	6	issn	issn	NOUN
cana-5959	93	7	:	:	PUNCT
cana-5959	93	8	1074	1074	NUM
cana-5959	93	9	-	-	PUNCT
cana-5959	93	10	133x	133x	NUM
cana-5959	93	11	vol	vol	VERB
cana-5959	93	12	32	32	NUM
cana-5959	93	13	no	no	NOUN
cana-5959	93	14	.	.	PUNCT
cana-5959	94	1	10s	10	NOUN
cana-5959	94	2	(	(	PUNCT
cana-5959	94	3	2025	2025	NUM
cana-5959	94	4	)	)	PUNCT
cana-5959	94	5	3197	3197	NUM
cana-5959	94	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	95	1	⚫	⚫	NOUN
cana-5959	95	2	the	the	DET
cana-5959	95	3	predicted	predict	VERB
cana-5959	95	4	versus	versus	ADP
cana-5959	95	5	observed	observed	ADJ
cana-5959	95	6	plots	plot	NOUN
cana-5959	95	7	(	(	PUNCT
cana-5959	95	8	figure	figure	NOUN
cana-5959	95	9	2	2	NUM
cana-5959	95	10	)	)	PUNCT
cana-5959	95	11	indicate	indicate	VERB
cana-5959	95	12	that	that	SCONJ
cana-5959	95	13	the	the	DET
cana-5959	95	14	predictions	prediction	NOUN
cana-5959	95	15	made	make	VERB
cana-5959	95	16	by	by	ADP
cana-5959	95	17	sp	sp	ADP
cana-5959	95	18	deviate	deviate	NOUN
cana-5959	95	19	from	from	ADP
cana-5959	95	20	the	the	DET
cana-5959	95	21	actual	actual	ADJ
cana-5959	95	22	counts	count	NOUN
cana-5959	95	23	,	,	PUNCT
cana-5959	95	24	particularly	particularly	ADV
cana-5959	95	25	at	at	ADP
cana-5959	95	26	elevated	elevated	ADJ
cana-5959	95	27	values	value	NOUN
cana-5959	95	28	.	.	PUNCT
cana-5959	96	1	in	in	ADP
cana-5959	96	2	contrast	contrast	NOUN
cana-5959	96	3	,	,	PUNCT
cana-5959	96	4	the	the	DET
cana-5959	96	5	predictions	prediction	NOUN
cana-5959	96	6	from	from	ADP
cana-5959	96	7	owpm	owpm	NOUN
cana-5959	96	8	and	and	CCONJ
cana-5959	96	9	nb	nb	NOUN
cana-5959	96	10	align	align	VERB
cana-5959	96	11	closely	closely	ADV
cana-5959	96	12	with	with	ADP
cana-5959	96	13	the	the	DET
cana-5959	96	14	observed	observe	VERB
cana-5959	96	15	data	datum	NOUN
cana-5959	96	16	.	.	PUNCT
cana-5959	97	1	10	10	NUM
cana-5959	97	2	%	%	NOUN
cana-5959	97	3	20	20	NUM
cana-5959	97	4	%	%	NOUN
cana-5959	97	5	5	5	NUM
cana-5959	97	6	%	%	NOUN
cana-5959	97	7	m	m	VERB
cana-5959	97	8	u	u	NOUN
cana-5959	97	9	ltip	ltip	X
cana-5959	97	10	le	le	X
cana-5959	97	11	s	s	VERB
cana-5959	97	12	i	i	NOUN
cana-5959	97	13	m	m	VERB
cana-5959	97	14	p	p	ADJ
cana-5959	97	15	le	le	X
cana-5959	97	16	negbin	negbin	PROPN
cana-5959	97	17	owpm	owpm	PROPN
cana-5959	97	18	poisson	poisson	PROPN
cana-5959	97	19	negbin	negbin	PROPN
cana-5959	97	20	owpm	owpm	PROPN
cana-5959	97	21	poisson	poisson	PROPN
cana-5959	97	22	negbin	negbin	PROPN
cana-5959	97	23	owpm	owpm	PROPN
cana-5959	97	24	poisson	poisson	PROPN
cana-5959	97	25	-5	-5	PROPN
cana-5959	97	26	0	0	NUM
cana-5959	97	27	5	5	NUM
cana-5959	97	28	10	10	NUM
cana-5959	97	29	15	15	NUM
cana-5959	97	30	20	20	NUM
cana-5959	97	31	-5	-5	NOUN
cana-5959	97	32	0	0	NUM
cana-5959	97	33	5	5	NUM
cana-5959	97	34	10	10	NUM
cana-5959	97	35	15	15	NUM
cana-5959	97	36	20	20	NUM
cana-5959	97	37	model	model	NOUN
cana-5959	97	38	r	r	NOUN
cana-5959	97	39	e	e	NOUN
cana-5959	97	40	s	s	X
cana-5959	97	41	i	i	PROPN
cana-5959	97	42	d	d	PROPN
cana-5959	97	43	u	u	PROPN
cana-5959	97	44	a	a	DET
cana-5959	97	45	l	l	NOUN
cana-5959	97	46	model	model	NOUN
cana-5959	98	1	negbin	negbin	PROPN
cana-5959	98	2	owpm	owpm	PROPN
cana-5959	98	3	poisson	poisson	PROPN
cana-5959	98	4	residual	residual	ADJ
cana-5959	98	5	distributions	distribution	NOUN
cana-5959	98	6	by	by	ADP
cana-5959	98	7	model	model	NOUN
cana-5959	98	8	,	,	PUNCT
cana-5959	98	9	outlier	outlier	ADJ
cana-5959	98	10	%	%	NOUN
cana-5959	98	11	,	,	PUNCT
cana-5959	98	12	and	and	CCONJ
cana-5959	98	13	regression	regression	NOUN
cana-5959	98	14	type	type	NOUN
cana-5959	98	15	communications	communication	NOUN
cana-5959	98	16	on	on	ADP
cana-5959	98	17	applied	apply	VERB
cana-5959	98	18	nonlinear	nonlinear	ADJ
cana-5959	98	19	analysis	analysis	NOUN
cana-5959	98	20	issn	issn	NOUN
cana-5959	98	21	:	:	PUNCT
cana-5959	98	22	1074	1074	NUM
cana-5959	98	23	-	-	PUNCT
cana-5959	98	24	133x	133x	NUM
cana-5959	98	25	vol	vol	VERB
cana-5959	98	26	32	32	NUM
cana-5959	98	27	no	no	NOUN
cana-5959	98	28	.	.	PUNCT
cana-5959	99	1	10s	10	NOUN
cana-5959	99	2	(	(	PUNCT
cana-5959	99	3	2025	2025	NUM
cana-5959	99	4	)	)	PUNCT
cana-5959	99	5	3198	3198	NUM
cana-5959	99	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	99	7	3.3	3.3	NUM
cana-5959	99	8	statistical	statistical	ADJ
cana-5959	99	9	testing	testing	NOUN
cana-5959	99	10	wilcoxon	wilcoxon	ADP
cana-5959	99	11	signed	sign	VERB
cana-5959	99	12	-	-	PUNCT
cana-5959	99	13	rank	rank	NOUN
cana-5959	99	14	tests	test	NOUN
cana-5959	99	15	validate	validate	VERB
cana-5959	99	16	that	that	SCONJ
cana-5959	99	17	the	the	DET
cana-5959	99	18	residual	residual	ADJ
cana-5959	99	19	errors	error	NOUN
cana-5959	99	20	,	,	PUNCT
cana-5959	99	21	measured	measure	VERB
cana-5959	99	22	by	by	ADP
cana-5959	99	23	both	both	DET
cana-5959	99	24	mse	mse	PROPN
cana-5959	99	25	and	and	CCONJ
cana-5959	99	26	mae	mae	PROPN
cana-5959	99	27	,	,	PUNCT
cana-5959	99	28	for	for	ADP
cana-5959	99	29	owpm	owpm	NOUN
cana-5959	99	30	and	and	CCONJ
cana-5959	99	31	nb	nb	NOUN
cana-5959	99	32	are	be	AUX
cana-5959	99	33	significantly	significantly	ADV
cana-5959	99	34	lower	low	ADJ
cana-5959	99	35	than	than	ADP
cana-5959	99	36	those	those	PRON
cana-5959	99	37	for	for	ADP
cana-5959	99	38	sp	sp	ADP
cana-5959	99	39	(	(	PUNCT
cana-5959	99	40	p<0.05	p<0.05	NOUN
cana-5959	99	41	)	)	PUNCT
cana-5959	99	42	at	at	ADP
cana-5959	99	43	moderate	moderate	ADJ
cana-5959	99	44	and	and	CCONJ
cana-5959	99	45	high	high	ADJ
cana-5959	99	46	outlier	outlier	NOUN
cana-5959	99	47	rates	rate	NOUN
cana-5959	99	48	,	,	PUNCT
cana-5959	99	49	thereby	thereby	ADV
cana-5959	99	50	confirming	confirm	VERB
cana-5959	99	51	the	the	DET
cana-5959	99	52	advantage	advantage	NOUN
cana-5959	99	53	in	in	ADP
cana-5959	99	54	robustness	robustness	NOUN
cana-5959	99	55	.	.	PUNCT
cana-5959	100	1	the	the	DET
cana-5959	100	2	differences	difference	NOUN
cana-5959	100	3	observed	observe	VERB
cana-5959	100	4	between	between	ADP
cana-5959	100	5	owpm	owpm	NOUN
cana-5959	100	6	and	and	CCONJ
cana-5959	100	7	nb	nb	NOUN
cana-5959	100	8	were	be	AUX
cana-5959	100	9	not	not	PART
cana-5959	100	10	statistically	statistically	ADV
cana-5959	100	11	significant	significant	ADJ
cana-5959	100	12	,	,	PUNCT
cana-5959	100	13	underscoring	underscore	VERB
cana-5959	100	14	their	their	PRON
cana-5959	100	15	similar	similar	ADJ
cana-5959	100	16	performance	performance	NOUN
cana-5959	100	17	.	.	PUNCT
cana-5959	101	1	type	type	NOUN
cana-5959	101	2	%	%	NOUN
cana-5959	101	3	comparison	comparison	NOUN
cana-5959	101	4	mse_pvalue	mse_pvalue	NOUN
cana-5959	101	5	mae_pvalue	mae_pvalue	X
cana-5959	101	6	simple	simple	ADJ
cana-5959	101	7	5	5	NUM
cana-5959	101	8	poisson	poisson	NOUN
cana-5959	101	9	vs	vs	ADP
cana-5959	101	10	owpm	owpm	NOUN
cana-5959	101	11	3.392392e-04	3.392392e-04	NUM
cana-5959	101	12	2.842396e-07	2.842396e-07	NUM
cana-5959	101	13	simple	simple	ADJ
cana-5959	101	14	5	5	NUM
cana-5959	101	15	poisson	poisson	NOUN
cana-5959	101	16	vs	vs	ADP
cana-5959	101	17	negbin	negbin	PROPN
cana-5959	101	18	1.006661e-02	1.006661e-02	NUM
cana-5959	101	19	2.329938e-02	2.329938e-02	NUM
cana-5959	101	20	simple	simple	ADJ
cana-5959	101	21	5	5	NUM
cana-5959	101	22	owpm	owpm	NOUN
cana-5959	101	23	vs	vs	ADP
cana-5959	101	24	negbin	negbin	PROPN
cana-5959	101	25	2.362335e-06	2.362335e-06	NUM
cana-5959	101	26	9.596613e-06	9.596613e-06	NUM
cana-5959	101	27	simple	simple	ADJ
cana-5959	101	28	10	10	NUM
cana-5959	101	29	poisson	poisson	NOUN
cana-5959	101	30	vs	vs	ADP
cana-5959	101	31	owpm	owpm	NOUN
cana-5959	101	32	8.925887e-05	8.925887e-05	NUM
cana-5959	101	33	3.465261e-05	3.465261e-05	NUM
cana-5959	101	34	simple	simple	ADJ
cana-5959	101	35	10	10	NUM
cana-5959	101	36	poisson	poisson	NOUN
cana-5959	101	37	vs	vs	ADP
cana-5959	101	38	negbin	negbin	PROPN
cana-5959	101	39	3.441930e-01	3.441930e-01	NUM
cana-5959	101	40	7.265278e-01	7.265278e-01	NUM
cana-5959	101	41	simple	simple	ADJ
cana-5959	101	42	10	10	NUM
cana-5959	101	43	owpm	owpm	NOUN
cana-5959	101	44	vs	vs	ADP
cana-5959	101	45	negbin	negbin	PROPN
cana-5959	101	46	2.570640e-07	2.570640e-07	NUM
cana-5959	101	47	1.031478e-07	1.031478e-07	NUM
cana-5959	101	48	simple	simple	ADJ
cana-5959	101	49	20	20	NUM
cana-5959	101	50	poisson	poisson	NOUN
cana-5959	101	51	vs	vs	ADP
cana-5959	101	52	owpm	owpm	NOUN
cana-5959	101	53	3.292939e-11	3.292939e-11	NUM
cana-5959	101	54	1.100278e-14	1.100278e-14	NUM
cana-5959	101	55	simple	simple	ADJ
cana-5959	101	56	20	20	NUM
cana-5959	101	57	poisson	poisson	NOUN
cana-5959	101	58	vs	vs	ADP
cana-5959	101	59	negbin	negbin	PROPN
cana-5959	101	60	5.336041e-04	5.336041e-04	NUM
cana-5959	101	61	9.960708e-06	9.960708e-06	NUM
cana-5959	101	62	simple	simple	ADJ
cana-5959	101	63	20	20	NUM
cana-5959	101	64	owpm	owpm	NOUN
cana-5959	101	65	vs	vs	ADP
cana-5959	101	66	negbin	negbin	PROPN
cana-5959	101	67	4.122090e-12	4.122090e-12	PROPN
cana-5959	101	68	1.105637e-14	1.105637e-14	NUM
cana-5959	101	69	multiple	multiple	ADJ
cana-5959	101	70	5	5	NUM
cana-5959	101	71	poisson	poisson	NOUN
cana-5959	101	72	vs	vs	ADP
cana-5959	101	73	owpm	owpm	NOUN
cana-5959	101	74	3.383611e-03	3.383611e-03	NUM
cana-5959	101	75	1.670056e-04	1.670056e-04	NUM
cana-5959	101	76	multiple	multiple	ADJ
cana-5959	101	77	5	5	NUM
cana-5959	101	78	poisson	poisson	NOUN
cana-5959	101	79	vs	vs	ADP
cana-5959	101	80	negbin	negbin	PROPN
cana-5959	101	81	4.456046e-01	4.456046e-01	NUM
cana-5959	101	82	4.806736e-01	4.806736e-01	NUM
cana-5959	101	83	multiple	multiple	ADJ
cana-5959	101	84	5	5	NUM
cana-5959	101	85	owpm	owpm	NOUN
cana-5959	101	86	vs	vs	ADP
cana-5959	101	87	negbin	negbin	PROPN
cana-5959	101	88	1.039817e-03	1.039817e-03	NUM
cana-5959	101	89	6.299624e-05	6.299624e-05	NUM
cana-5959	101	90	multiple	multiple	ADJ
cana-5959	101	91	10	10	NUM
cana-5959	101	92	poisson	poisson	NOUN
cana-5959	101	93	vs	vs	ADP
cana-5959	101	94	owpm	owpm	NOUN
cana-5959	101	95	1.997290e-05	1.997290e-05	NUM
cana-5959	101	96	3.540222e-07	3.540222e-07	NUM
cana-5959	101	97	multiple	multiple	ADJ
cana-5959	101	98	10	10	NUM
cana-5959	101	99	poisson	poisson	NOUN
cana-5959	101	100	vs	vs	ADP
cana-5959	101	101	negbin	negbin	PROPN
cana-5959	101	102	1.575446e-01	1.575446e-01	NUM
cana-5959	101	103	1.018128e-01	1.018128e-01	NUM
cana-5959	101	104	multiple	multiple	ADJ
cana-5959	101	105	10	10	NUM
cana-5959	101	106	owpm	owpm	NOUN
cana-5959	101	107	vs	vs	ADP
cana-5959	101	108	negbin	negbin	PROPN
cana-5959	101	109	6.447821e-06	6.447821e-06	NUM
cana-5959	101	110	6.736350e-11	6.736350e-11	NUM
cana-5959	101	111	multiple	multiple	ADJ
cana-5959	101	112	20	20	NUM
cana-5959	101	113	poisson	poisson	NOUN
cana-5959	101	114	vs	vs	ADP
cana-5959	101	115	owpm	owpm	NOUN
cana-5959	101	116	1.394064e-09	1.394064e-09	NUM
cana-5959	101	117	1.643112e-13	1.643112e-13	NUM
cana-5959	101	118	multiple	multiple	ADJ
cana-5959	101	119	20	20	NUM
cana-5959	101	120	poisson	poisson	NOUN
cana-5959	101	121	vs	vs	ADP
cana-5959	101	122	negbin	negbin	PROPN
cana-5959	101	123	5.449034e-02	5.449034e-02	NUM
cana-5959	101	124	5.164469e-03	5.164469e-03	NUM
cana-5959	101	125	multiple	multiple	ADJ
cana-5959	101	126	20	20	NUM
cana-5959	101	127	owpm	owpm	NOUN
cana-5959	101	128	vs	vs	ADP
cana-5959	101	129	negbin	negbin	PROPN
cana-5959	101	130	4.533468e-11	4.533468e-11	NUM
cana-5959	101	131	7.247443e-14	7.247443e-14	NUM
cana-5959	101	132	4	4	NUM
cana-5959	101	133	.	.	PUNCT
cana-5959	102	1	discussion	discussion	NOUN
cana-5959	102	2	our	our	PRON
cana-5959	102	3	findings	finding	NOUN
cana-5959	102	4	validate	validate	VERB
cana-5959	102	5	the	the	DET
cana-5959	102	6	negative	negative	ADJ
cana-5959	102	7	impact	impact	NOUN
cana-5959	102	8	of	of	ADP
cana-5959	102	9	outliers	outlier	NOUN
cana-5959	102	10	on	on	ADP
cana-5959	102	11	the	the	DET
cana-5959	102	12	efficacy	efficacy	NOUN
cana-5959	102	13	of	of	ADP
cana-5959	102	14	poisson	poisson	PROPN
cana-5959	102	15	regression	regression	PROPN
cana-5959	102	16	,	,	PUNCT
cana-5959	102	17	resulting	result	VERB
cana-5959	102	18	in	in	ADP
cana-5959	102	19	overdispersion	overdispersion	NOUN
cana-5959	102	20	and	and	CCONJ
cana-5959	102	21	inadequate	inadequate	ADJ
cana-5959	102	22	fit	fit	NOUN
cana-5959	102	23	,	,	PUNCT
cana-5959	102	24	which	which	PRON
cana-5959	102	25	aligns	align	VERB
cana-5959	102	26	with	with	ADP
cana-5959	102	27	existing	exist	VERB
cana-5959	102	28	research	research	NOUN
cana-5959	102	29	(	(	PUNCT
cana-5959	102	30	ver	ver	PROPN
cana-5959	102	31	hoef&boveng	hoef&boveng	PROPN
cana-5959	102	32	,	,	PUNCT
cana-5959	102	33	2007	2007	NUM
cana-5959	102	34	;	;	PUNCT
cana-5959	102	35	hilbe	hilbe	NOUN
cana-5959	102	36	,	,	PUNCT
cana-5959	102	37	2014	2014	NUM
cana-5959	102	38	)	)	PUNCT
cana-5959	102	39	.	.	PUNCT
cana-5959	103	1	the	the	DET
cana-5959	103	2	owpm	owpm	NOUN
cana-5959	103	3	methodology	methodology	NOUN
cana-5959	103	4	effectively	effectively	ADV
cana-5959	103	5	addresses	address	VERB
cana-5959	103	6	this	this	DET
cana-5959	103	7	issue	issue	NOUN
cana-5959	103	8	by	by	ADP
cana-5959	103	9	downweighting	downweighte	VERB
cana-5959	103	10	significant	significant	ADJ
cana-5959	103	11	observations	observation	NOUN
cana-5959	103	12	based	base	VERB
cana-5959	103	13	on	on	ADP
cana-5959	103	14	cook	cook	PROPN
cana-5959	103	15	’s	’s	PART
cana-5959	103	16	distance	distance	NOUN
cana-5959	103	17	,	,	PUNCT
cana-5959	103	18	thereby	thereby	ADV
cana-5959	103	19	enhancing	enhance	VERB
cana-5959	103	20	parameter	parameter	NOUN
cana-5959	103	21	estimation	estimation	NOUN
cana-5959	103	22	and	and	CCONJ
cana-5959	103	23	predictive	predictive	ADJ
cana-5959	103	24	accuracy	accuracy	NOUN
cana-5959	103	25	.	.	PUNCT
cana-5959	104	1	communications	communication	NOUN
cana-5959	104	2	on	on	ADP
cana-5959	104	3	applied	apply	VERB
cana-5959	104	4	nonlinear	nonlinear	ADJ
cana-5959	104	5	analysis	analysis	NOUN
cana-5959	104	6	issn	issn	NOUN
cana-5959	104	7	:	:	PUNCT
cana-5959	104	8	1074	1074	NUM
cana-5959	104	9	-	-	PUNCT
cana-5959	104	10	133x	133x	NUM
cana-5959	104	11	vol	vol	VERB
cana-5959	104	12	32	32	NUM
cana-5959	104	13	no	no	NOUN
cana-5959	104	14	.	.	PUNCT
cana-5959	105	1	10s	10	NOUN
cana-5959	105	2	(	(	PUNCT
cana-5959	105	3	2025	2025	NUM
cana-5959	105	4	)	)	PUNCT
cana-5959	105	5	3199	3199	NUM
cana-5959	105	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	106	1	in	in	ADP
cana-5959	106	2	contrast	contrast	NOUN
cana-5959	106	3	to	to	ADP
cana-5959	106	4	the	the	DET
cana-5959	106	5	negative	negative	ADJ
cana-5959	106	6	binomial	binomial	ADJ
cana-5959	106	7	model	model	NOUN
cana-5959	106	8	,	,	PUNCT
cana-5959	106	9	which	which	PRON
cana-5959	106	10	addresses	address	VERB
cana-5959	106	11	overdispersion	overdispersion	NOUN
cana-5959	106	12	through	through	ADP
cana-5959	106	13	parametric	parametric	ADJ
cana-5959	106	14	means	mean	NOUN
cana-5959	106	15	,	,	PUNCT
cana-5959	106	16	owpm	owpm	NOUN
cana-5959	106	17	presents	present	VERB
cana-5959	106	18	a	a	DET
cana-5959	106	19	computationally	computationally	ADV
cana-5959	106	20	simple	simple	ADJ
cana-5959	106	21	and	and	CCONJ
cana-5959	106	22	adaptable	adaptable	ADJ
cana-5959	106	23	alternative	alternative	NOUN
cana-5959	106	24	that	that	PRON
cana-5959	106	25	does	do	AUX
cana-5959	106	26	not	not	PART
cana-5959	106	27	necessitate	necessitate	VERB
cana-5959	106	28	distributional	distributional	ADJ
cana-5959	106	29	assumptions	assumption	NOUN
cana-5959	106	30	regarding	regard	VERB
cana-5959	106	31	the	the	DET
cana-5959	106	32	source	source	NOUN
cana-5959	106	33	of	of	ADP
cana-5959	106	34	overdispersion	overdispersion	NOUN
cana-5959	106	35	(	(	PUNCT
cana-5959	106	36	jin	jin	NOUN
cana-5959	106	37	et	et	PROPN
cana-5959	106	38	al	al	PROPN
cana-5959	106	39	.	.	PROPN
cana-5959	106	40	,	,	PUNCT
cana-5959	106	41	2020	2020	NUM
cana-5959	106	42	)	)	PUNCT
cana-5959	106	43	.	.	PUNCT
cana-5959	107	1	this	this	DET
cana-5959	107	2	characteristic	characteristic	ADJ
cana-5959	107	3	renders	render	VERB
cana-5959	107	4	owpm	owpm	NOUN
cana-5959	107	5	particularly	particularly	ADV
cana-5959	107	6	appealing	appealing	ADJ
cana-5959	107	7	when	when	SCONJ
cana-5959	107	8	overdispersion	overdispersion	NOUN
cana-5959	107	9	is	be	AUX
cana-5959	107	10	primarily	primarily	ADV
cana-5959	107	11	attributable	attributable	ADJ
cana-5959	107	12	to	to	ADP
cana-5959	107	13	outliers	outlier	NOUN
cana-5959	107	14	rather	rather	ADV
cana-5959	107	15	than	than	ADP
cana-5959	107	16	unobserved	unobserved	ADJ
cana-5959	107	17	heterogeneity	heterogeneity	NOUN
cana-5959	107	18	.	.	PUNCT
cana-5959	108	1	the	the	DET
cana-5959	108	2	enhanced	enhanced	ADJ
cana-5959	108	3	simulation	simulation	NOUN
cana-5959	108	4	that	that	PRON
cana-5959	108	5	includes	include	VERB
cana-5959	108	6	zero	zero	NUM
cana-5959	108	7	inflation	inflation	NOUN
cana-5959	108	8	,	,	PUNCT
cana-5959	108	9	heteroscedasticity	heteroscedasticity	NOUN
cana-5959	108	10	,	,	PUNCT
cana-5959	108	11	and	and	CCONJ
cana-5959	108	12	correlated	correlate	VERB
cana-5959	108	13	covariates	covariate	NOUN
cana-5959	108	14	captures	capture	VERB
cana-5959	108	15	the	the	DET
cana-5959	108	16	realistic	realistic	ADJ
cana-5959	108	17	complexities	complexity	NOUN
cana-5959	108	18	encountered	encounter	VERB
cana-5959	108	19	in	in	ADP
cana-5959	108	20	count	count	NOUN
cana-5959	108	21	data	datum	NOUN
cana-5959	108	22	modeling	modeling	NOUN
cana-5959	108	23	(	(	PUNCT
cana-5959	108	24	zeileis	zeileis	PROPN
cana-5959	108	25	et	et	PROPN
cana-5959	108	26	al	al	PROPN
cana-5959	108	27	.	.	PROPN
cana-5959	108	28	,	,	PUNCT
cana-5959	108	29	2008	2008	NUM
cana-5959	108	30	;	;	PUNCT
cana-5959	108	31	o’hara&kotze	o’hara&kotze	NUM
cana-5959	108	32	,	,	PUNCT
cana-5959	108	33	2010	2010	NUM
cana-5959	108	34	)	)	PUNCT
cana-5959	108	35	,	,	PUNCT
cana-5959	108	36	thereby	thereby	ADV
cana-5959	108	37	reinforcing	reinforce	VERB
cana-5959	108	38	the	the	DET
cana-5959	108	39	applicability	applicability	NOUN
cana-5959	108	40	of	of	ADP
cana-5959	108	41	our	our	PRON
cana-5959	108	42	results	result	NOUN
cana-5959	108	43	.	.	PUNCT
cana-5959	109	1	limitations	limitation	NOUN
cana-5959	109	2	and	and	CCONJ
cana-5959	109	3	future	future	ADJ
cana-5959	109	4	work	work	NOUN
cana-5959	109	5	•	•	ADP
cana-5959	109	6	the	the	DET
cana-5959	109	7	application	application	NOUN
cana-5959	109	8	of	of	ADP
cana-5959	109	9	real	real	ADJ
cana-5959	109	10	data	datum	NOUN
cana-5959	109	11	is	be	AUX
cana-5959	109	12	essential	essential	ADJ
cana-5959	109	13	to	to	PART
cana-5959	109	14	validate	validate	VERB
cana-5959	109	15	practical	practical	ADJ
cana-5959	109	16	effectiveness	effectiveness	NOUN
cana-5959	109	17	.	.	PUNCT
cana-5959	110	1	•	•	NUM
cana-5959	110	2	expanding	expand	VERB
cana-5959	110	3	owpm	owpm	NOUN
cana-5959	110	4	to	to	PART
cana-5959	110	5	encompass	encompass	VERB
cana-5959	110	6	zero	zero	NUM
cana-5959	110	7	-	-	PUNCT
cana-5959	110	8	inflated	inflate	VERB
cana-5959	110	9	and	and	CCONJ
cana-5959	110	10	hurdle	hurdle	NOUN
cana-5959	110	11	models	model	NOUN
cana-5959	110	12	could	could	AUX
cana-5959	110	13	further	far	ADV
cana-5959	110	14	bolster	bolster	VERB
cana-5959	110	15	its	its	PRON
cana-5959	110	16	robustness	robustness	NOUN
cana-5959	110	17	.	.	PUNCT
cana-5959	111	1	•	•	NUM
cana-5959	111	2	the	the	DET
cana-5959	111	3	integration	integration	NOUN
cana-5959	111	4	of	of	ADP
cana-5959	111	5	bayesian	bayesian	NOUN
cana-5959	111	6	weighting	weighting	NOUN
cana-5959	111	7	methods	method	NOUN
cana-5959	111	8	may	may	AUX
cana-5959	111	9	provide	provide	VERB
cana-5959	111	10	additional	additional	ADJ
cana-5959	111	11	benefits	benefit	NOUN
cana-5959	111	12	.	.	PUNCT
cana-5959	112	1	5	5	X
cana-5959	112	2	.	.	X
cana-5959	112	3	conclusion	conclusion	NOUN
cana-5959	112	4	this	this	DET
cana-5959	112	5	research	research	NOUN
cana-5959	112	6	illustrates	illustrate	VERB
cana-5959	112	7	that	that	SCONJ
cana-5959	112	8	an	an	DET
cana-5959	112	9	outlier	outlier	NOUN
cana-5959	112	10	-	-	PUNCT
cana-5959	112	11	weighted	weight	VERB
cana-5959	112	12	poisson	poisson	NOUN
cana-5959	112	13	regression	regression	NOUN
cana-5959	112	14	model	model	NOUN
cana-5959	112	15	serves	serve	VERB
cana-5959	112	16	as	as	ADP
cana-5959	112	17	a	a	DET
cana-5959	112	18	robust	robust	ADJ
cana-5959	112	19	and	and	CCONJ
cana-5959	112	20	effective	effective	ADJ
cana-5959	112	21	approach	approach	NOUN
cana-5959	112	22	for	for	ADP
cana-5959	112	23	modeling	model	VERB
cana-5959	112	24	overdispersed	overdisperse	VERB
cana-5959	112	25	count	count	NOUN
cana-5959	112	26	data	datum	NOUN
cana-5959	112	27	influenced	influence	VERB
cana-5959	112	28	by	by	ADP
cana-5959	112	29	outliers	outlier	NOUN
cana-5959	112	30	.	.	PUNCT
cana-5959	113	1	our	our	PRON
cana-5959	113	2	simulations	simulation	NOUN
cana-5959	113	3	indicate	indicate	VERB
cana-5959	113	4	that	that	SCONJ
cana-5959	113	5	owpm	owpm	NOUN
cana-5959	113	6	surpasses	surpass	VERB
cana-5959	113	7	the	the	DET
cana-5959	113	8	traditional	traditional	ADJ
cana-5959	113	9	poisson	poisson	NOUN
cana-5959	113	10	model	model	NOUN
cana-5959	113	11	and	and	CCONJ
cana-5959	113	12	performs	perform	VERB
cana-5959	113	13	competitively	competitively	ADV
cana-5959	113	14	with	with	ADP
cana-5959	113	15	negative	negative	ADJ
cana-5959	113	16	binomial	binomial	ADJ
cana-5959	113	17	regression	regression	NOUN
cana-5959	113	18	,	,	PUNCT
cana-5959	113	19	establishing	establish	VERB
cana-5959	113	20	it	it	PRON
cana-5959	113	21	as	as	ADP
cana-5959	113	22	a	a	DET
cana-5959	113	23	valuable	valuable	ADJ
cana-5959	113	24	resource	resource	NOUN
cana-5959	113	25	for	for	ADP
cana-5959	113	26	practitioners	practitioner	NOUN
cana-5959	113	27	.	.	PUNCT
cana-5959	114	1	references	reference	NOUN
cana-5959	114	2	overdispersion&poisson	overdispersion&poisson	INTJ
cana-5959	114	3	regression	regression	NOUN
cana-5959	114	4	theory	theory	NOUN
cana-5959	114	5	:	:	PUNCT
cana-5959	114	6	1	1	X
cana-5959	114	7	.	.	X
cana-5959	114	8	cameron	cameron	PROPN
cana-5959	114	9	,	,	PUNCT
cana-5959	114	10	a.c	a.c	PROPN
cana-5959	114	11	.	.	PROPN
cana-5959	114	12	,	,	PUNCT
cana-5959	114	13	&	&	CCONJ
cana-5959	114	14	trivedi	trivedi	PROPN
cana-5959	114	15	,	,	PUNCT
cana-5959	114	16	p.k	p.k	PROPN
cana-5959	114	17	.	.	PROPN
cana-5959	114	18	(	(	PUNCT
cana-5959	114	19	1990	1990	NUM
cana-5959	114	20	)	)	PUNCT
cana-5959	114	21	.	.	PUNCT
cana-5959	115	1	regression	regression	NOUN
cana-5959	115	2	-	-	PUNCT
cana-5959	115	3	based	base	VERB
cana-5959	115	4	tests	test	NOUN
cana-5959	115	5	for	for	ADP
cana-5959	115	6	overdispersion	overdispersion	NOUN
cana-5959	115	7	in	in	ADP
cana-5959	115	8	the	the	DET
cana-5959	115	9	poisson	poisson	PROPN
cana-5959	115	10	model	model	PROPN
cana-5959	115	11	.	.	PUNCT
cana-5959	116	1	journal	journal	PROPN
cana-5959	116	2	of	of	ADP
cana-5959	116	3	econometrics	econometrics	PROPN
cana-5959	116	4	,	,	PUNCT
cana-5959	116	5	46(3	46(3	NOUN
cana-5959	116	6	)	)	PUNCT
cana-5959	116	7	,	,	PUNCT
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cana-5959	116	9	-	-	SYM
cana-5959	116	10	364	364	NUM
cana-5959	116	11	.	.	NOUN
cana-5959	117	1	2	2	NUM
cana-5959	117	2	.	.	X
cana-5959	117	3	cameron	cameron	PROPN
cana-5959	117	4	,	,	PUNCT
cana-5959	117	5	a.c	a.c	PROPN
cana-5959	117	6	.	.	PROPN
cana-5959	117	7	,	,	PUNCT
cana-5959	117	8	&	&	CCONJ
cana-5959	117	9	trivedi	trivedi	PROPN
cana-5959	117	10	,	,	PUNCT
cana-5959	117	11	p.k	p.k	PROPN
cana-5959	117	12	.	.	PROPN
cana-5959	117	13	(	(	PUNCT
cana-5959	117	14	2013	2013	NUM
cana-5959	117	15	)	)	PUNCT
cana-5959	117	16	.	.	PUNCT
cana-5959	118	1	regression	regression	VERB
cana-5959	118	2	analysis	analysis	NOUN
cana-5959	118	3	of	of	ADP
cana-5959	118	4	count	count	NOUN
cana-5959	118	5	data	datum	NOUN
cana-5959	118	6	(	(	PUNCT
cana-5959	118	7	2nd	2nd	ADJ
cana-5959	118	8	ed	ed	NOUN
cana-5959	118	9	.	.	PUNCT
cana-5959	118	10	)	)	PUNCT
cana-5959	118	11	.	.	PUNCT
cana-5959	119	1	cambridge	cambridge	PROPN
cana-5959	119	2	university	university	PROPN
cana-5959	119	3	press	press	NOUN
cana-5959	119	4	.	.	PUNCT
cana-5959	120	1	3	3	X
cana-5959	120	2	.	.	X
cana-5959	120	3	dean	dean	PROPN
cana-5959	120	4	,	,	PUNCT
cana-5959	120	5	c.b	c.b	PROPN
cana-5959	120	6	.	.	PROPN
cana-5959	120	7	(	(	PUNCT
cana-5959	120	8	1992	1992	NUM
cana-5959	120	9	)	)	PUNCT
cana-5959	120	10	.	.	PUNCT
cana-5959	121	1	testing	test	VERB
cana-5959	121	2	for	for	ADP
cana-5959	121	3	overdispersion	overdispersion	NOUN
cana-5959	121	4	in	in	ADP
cana-5959	121	5	poisson	poisson	NOUN
cana-5959	121	6	and	and	CCONJ
cana-5959	121	7	binomial	binomial	ADJ
cana-5959	121	8	regression	regression	NOUN
cana-5959	121	9	models	model	NOUN
cana-5959	121	10	.	.	PUNCT
cana-5959	122	1	journal	journal	NOUN
cana-5959	122	2	of	of	ADP
cana-5959	122	3	the	the	DET
cana-5959	122	4	american	american	PROPN
cana-5959	122	5	statistical	statistical	PROPN
cana-5959	122	6	association	association	PROPN
cana-5959	122	7	,	,	PUNCT
cana-5959	122	8	87(418	87(418	NOUN
cana-5959	122	9	)	)	PUNCT
cana-5959	122	10	,	,	PUNCT
cana-5959	122	11	451	451	NUM
cana-5959	122	12	-	-	SYM
cana-5959	122	13	457	457	NUM
cana-5959	122	14	.	.	PUNCT
cana-5959	123	1	4	4	NUM
cana-5959	123	2	.	.	X
cana-5959	123	3	hilbe	hilbe	NOUN
cana-5959	123	4	,	,	PUNCT
cana-5959	123	5	j.m	j.m	PROPN
cana-5959	123	6	.	.	PROPN
cana-5959	123	7	(	(	PUNCT
cana-5959	123	8	2011	2011	NUM
cana-5959	123	9	)	)	PUNCT
cana-5959	123	10	.	.	PUNCT
cana-5959	124	1	negative	negative	ADJ
cana-5959	124	2	binomial	binomial	ADJ
cana-5959	124	3	regression	regression	NOUN
cana-5959	124	4	(	(	PUNCT
cana-5959	124	5	2nd	2nd	ADJ
cana-5959	124	6	ed	ed	NOUN
cana-5959	124	7	.	.	PUNCT
cana-5959	124	8	)	)	PUNCT
cana-5959	124	9	.	.	PUNCT
cana-5959	125	1	cambridge	cambridge	PROPN
cana-5959	125	2	university	university	PROPN
cana-5959	125	3	press	press	NOUN
cana-5959	125	4	.	.	PUNCT
cana-5959	126	1	5	5	X
cana-5959	126	2	.	.	X
cana-5959	126	3	lawless	lawless	ADJ
cana-5959	126	4	,	,	PUNCT
cana-5959	126	5	j.f	j.f	PROPN
cana-5959	126	6	.	.	PROPN
cana-5959	126	7	(	(	PUNCT
cana-5959	126	8	1987	1987	NUM
cana-5959	126	9	)	)	PUNCT
cana-5959	126	10	.	.	PUNCT
cana-5959	127	1	negative	negative	ADJ
cana-5959	127	2	binomial	binomial	ADJ
cana-5959	127	3	and	and	CCONJ
cana-5959	127	4	mixed	mixed	ADJ
cana-5959	127	5	poisson	poisson	NOUN
cana-5959	127	6	regression	regression	NOUN
cana-5959	127	7	.	.	PUNCT
cana-5959	128	1	the	the	DET
cana-5959	128	2	canadian	canadian	ADJ
cana-5959	128	3	journal	journal	PROPN
cana-5959	128	4	of	of	ADP
cana-5959	128	5	statistics	statistic	NOUN
cana-5959	128	6	,	,	PUNCT
cana-5959	128	7	15(3	15(3	NUM
cana-5959	128	8	)	)	PUNCT
cana-5959	128	9	,	,	PUNCT
cana-5959	128	10	209	209	NUM
cana-5959	128	11	-	-	SYM
cana-5959	128	12	225	225	NUM
cana-5959	128	13	.	.	NOUN
cana-5959	129	1	6	6	NUM
cana-5959	129	2	.	.	X
cana-5959	129	3	rousseeuw	rousseeuw	PROPN
cana-5959	129	4	,	,	PUNCT
cana-5959	129	5	p.j	p.j	PROPN
cana-5959	129	6	.	.	PROPN
cana-5959	129	7	,	,	PUNCT
cana-5959	129	8	&	&	CCONJ
cana-5959	129	9	leroy	leroy	PROPN
cana-5959	129	10	,	,	PUNCT
cana-5959	129	11	a.m.	a.m.	PROPN
cana-5959	129	12	(	(	PUNCT
cana-5959	129	13	1987	1987	NUM
cana-5959	129	14	)	)	PUNCT
cana-5959	129	15	.	.	PUNCT
cana-5959	130	1	robust	robust	ADJ
cana-5959	130	2	regression	regression	NOUN
cana-5959	130	3	and	and	CCONJ
cana-5959	130	4	outlier	outlier	NOUN
cana-5959	130	5	detection	detection	NOUN
cana-5959	130	6	.	.	PUNCT
cana-5959	131	1	wiley	wiley	PROPN
cana-5959	131	2	.	.	PUNCT
cana-5959	132	1	7	7	X
cana-5959	132	2	.	.	X
cana-5959	132	3	müller	müller	PROPN
cana-5959	132	4	,	,	PUNCT
cana-5959	132	5	h.g	h.g	PROPN
cana-5959	132	6	.	.	PROPN
cana-5959	132	7	,	,	PUNCT
cana-5959	132	8	&	&	CCONJ
cana-5959	132	9	welsh	welsh	PROPN
cana-5959	132	10	,	,	PUNCT
cana-5959	132	11	a.h	a.h	PROPN
cana-5959	132	12	.	.	PROPN
cana-5959	132	13	(	(	PUNCT
cana-5959	132	14	2005	2005	NUM
cana-5959	132	15	)	)	PUNCT
cana-5959	132	16	.	.	PUNCT
cana-5959	133	1	robust	robust	ADJ
cana-5959	133	2	estimation	estimation	NOUN
cana-5959	133	3	for	for	ADP
cana-5959	133	4	generalized	generalized	ADJ
cana-5959	133	5	linear	linear	NOUN
cana-5959	133	6	models	model	NOUN
cana-5959	133	7	.	.	PUNCT
cana-5959	134	1	journal	journal	NOUN
cana-5959	134	2	of	of	ADP
cana-5959	134	3	the	the	DET
cana-5959	134	4	american	american	PROPN
cana-5959	134	5	statistical	statistical	PROPN
cana-5959	134	6	association	association	PROPN
cana-5959	134	7	,	,	PUNCT
cana-5959	134	8	100(471	100(471	NUM
cana-5959	134	9	)	)	PUNCT
cana-5959	134	10	,	,	PUNCT
cana-5959	134	11	238	238	NUM
cana-5959	134	12	-	-	SYM
cana-5959	134	13	251	251	NUM
cana-5959	134	14	.	.	PUNCT
cana-5959	135	1	8	8	NUM
cana-5959	135	2	.	.	X
cana-5959	135	3	gervini	gervini	PROPN
cana-5959	135	4	,	,	PUNCT
cana-5959	135	5	d.	d.	PROPN
cana-5959	135	6	,	,	PUNCT
cana-5959	135	7	&	&	CCONJ
cana-5959	135	8	yohai	yohai	PROPN
cana-5959	135	9	,	,	PUNCT
cana-5959	135	10	v.j	v.j	PROPN
cana-5959	135	11	.	.	PROPN
cana-5959	135	12	(	(	PUNCT
cana-5959	135	13	2002	2002	NUM
cana-5959	135	14	)	)	PUNCT
cana-5959	135	15	.	.	PUNCT
cana-5959	136	1	a	a	DET
cana-5959	136	2	class	class	NOUN
cana-5959	136	3	of	of	ADP
cana-5959	136	4	robust	robust	ADJ
cana-5959	136	5	and	and	CCONJ
cana-5959	136	6	fully	fully	ADV
cana-5959	136	7	efficient	efficient	ADJ
cana-5959	136	8	regression	regression	NOUN
cana-5959	136	9	estimators	estimator	NOUN
cana-5959	136	10	.	.	PUNCT
cana-5959	137	1	the	the	DET
cana-5959	137	2	annals	annal	NOUN
cana-5959	137	3	of	of	ADP
cana-5959	137	4	statistics	statistic	NOUN
cana-5959	137	5	,	,	PUNCT
cana-5959	137	6	30(2	30(2	NUM
cana-5959	137	7	)	)	PUNCT
cana-5959	137	8	,	,	PUNCT
cana-5959	137	9	583	583	NUM
cana-5959	137	10	-	-	SYM
cana-5959	137	11	616	616	NUM
cana-5959	137	12	.	.	PUNCT
cana-5959	138	1	communications	communication	NOUN
cana-5959	138	2	on	on	ADP
cana-5959	138	3	applied	apply	VERB
cana-5959	138	4	nonlinear	nonlinear	ADJ
cana-5959	138	5	analysis	analysis	NOUN
cana-5959	138	6	issn	issn	NOUN
cana-5959	138	7	:	:	PUNCT
cana-5959	138	8	1074	1074	NUM
cana-5959	138	9	-	-	PUNCT
cana-5959	138	10	133x	133x	NUM
cana-5959	138	11	vol	vol	VERB
cana-5959	138	12	32	32	NUM
cana-5959	138	13	no	no	NOUN
cana-5959	138	14	.	.	PUNCT
cana-5959	139	1	10s	10	NOUN
cana-5959	139	2	(	(	PUNCT
cana-5959	139	3	2025	2025	NUM
cana-5959	139	4	)	)	PUNCT
cana-5959	139	5	3200	3200	NUM
cana-5959	140	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5959	140	2	9	9	NUM
cana-5959	140	3	.	.	X
cana-5959	140	4	ma	ma	PROPN
cana-5959	140	5	,	,	PUNCT
cana-5959	140	6	y.	y.	PROPN
cana-5959	140	7	,	,	PUNCT
cana-5959	140	8	jin	jin	NOUN
cana-5959	140	9	,	,	PUNCT
cana-5959	140	10	x.	x.	PROPN
cana-5959	140	11	,	,	PUNCT
cana-5959	140	12	&	&	CCONJ
cana-5959	140	13	wang	wang	PROPN
cana-5959	140	14	,	,	PUNCT
cana-5959	140	15	h.	h.	PROPN
cana-5959	140	16	(	(	PUNCT
cana-5959	140	17	2017	2017	NUM
cana-5959	140	18	)	)	PUNCT
cana-5959	140	19	.	.	PUNCT
cana-5959	141	1	robust	robust	ADJ
cana-5959	141	2	poisson	poisson	NOUN
cana-5959	141	3	regression	regression	NOUN
cana-5959	141	4	with	with	ADP
cana-5959	141	5	outlier	outlier	ADJ
cana-5959	141	6	detection	detection	NOUN
cana-5959	141	7	.	.	PUNCT
cana-5959	142	1	computational	computational	ADJ
cana-5959	142	2	statistics&data	statistics&data	PROPN
cana-5959	142	3	analysis	analysis	NOUN
cana-5959	142	4	,	,	PUNCT
cana-5959	142	5	105	105	NUM
cana-5959	142	6	,	,	PUNCT
cana-5959	142	7	95	95	NUM
cana-5959	142	8	-	-	SYM
cana-5959	142	9	105	105	NUM
cana-5959	142	10	.	.	PUNCT
cana-5959	142	11	10	10	NUM
cana-5959	142	12	.	.	PUNCT
cana-5959	143	1	jin	jin	NOUN
cana-5959	143	2	,	,	PUNCT
cana-5959	143	3	x.	x.	PROPN
cana-5959	143	4	,	,	PUNCT
cana-5959	143	5	ma	ma	PROPN
cana-5959	143	6	,	,	PUNCT
cana-5959	143	7	y.	y.	PROPN
cana-5959	143	8	,	,	PUNCT
cana-5959	143	9	&	&	CCONJ
cana-5959	143	10	zhao	zhao	PROPN
cana-5959	143	11	,	,	PUNCT
cana-5959	143	12	h.	h.	PROPN
cana-5959	143	13	(	(	PUNCT
cana-5959	143	14	2020	2020	NUM
cana-5959	143	15	)	)	PUNCT
cana-5959	143	16	.	.	PUNCT
cana-5959	144	1	robust	robust	ADJ
cana-5959	144	2	generalized	generalize	VERB
cana-5959	144	3	linear	linear	NOUN
cana-5959	144	4	models	model	NOUN
cana-5959	144	5	via	via	ADP
cana-5959	144	6	weighted	weighted	ADJ
cana-5959	144	7	likelihood	likelihood	NOUN
cana-5959	144	8	.	.	PUNCT
cana-5959	145	1	statistics	statistic	NOUN
cana-5959	145	2	and	and	CCONJ
cana-5959	145	3	computing	computing	NOUN
cana-5959	145	4	,	,	PUNCT
cana-5959	145	5	30(4	30(4	NUM
cana-5959	145	6	)	)	PUNCT
cana-5959	145	7	,	,	PUNCT
cana-5959	145	8	1077	1077	NUM
cana-5959	145	9	-	-	SYM
cana-5959	145	10	1090	1090	NUM
cana-5959	145	11	.	.	PUNCT
cana-5959	146	1	11	11	NUM
cana-5959	146	2	.	.	PUNCT
cana-5959	147	1	lambert	lambert	PROPN
cana-5959	147	2	,	,	PUNCT
cana-5959	147	3	d.	d.	PROPN
cana-5959	147	4	(	(	PUNCT
cana-5959	147	5	1992	1992	NUM
cana-5959	147	6	)	)	PUNCT
cana-5959	147	7	.	.	PUNCT
cana-5959	148	1	zero	zero	NUM
cana-5959	148	2	-	-	PUNCT
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cana-5959	148	4	poisson	poisson	NOUN
cana-5959	148	5	regression	regression	NOUN
cana-5959	148	6	,	,	PUNCT
cana-5959	148	7	with	with	ADP
cana-5959	148	8	an	an	DET
cana-5959	148	9	application	application	NOUN
cana-5959	148	10	to	to	ADP
cana-5959	148	11	defects	defect	NOUN
cana-5959	148	12	in	in	ADP
cana-5959	148	13	manufacturing	manufacturing	NOUN
cana-5959	148	14	.	.	PUNCT
cana-5959	149	1	technometrics	technometric	NOUN
cana-5959	149	2	,	,	PUNCT
cana-5959	149	3	34(1	34(1	NUM
cana-5959	149	4	)	)	PUNCT
cana-5959	149	5	,	,	PUNCT
cana-5959	149	6	1	1	NUM
cana-5959	149	7	-	-	SYM
cana-5959	149	8	14	14	NUM
cana-5959	149	9	.	.	PUNCT
cana-5959	149	10	12	12	NUM
cana-5959	149	11	.	.	PUNCT
cana-5959	150	1	zeileis	zeileis	PROPN
cana-5959	150	2	,	,	PUNCT
cana-5959	150	3	a.	a.	NOUN
cana-5959	150	4	,	,	PUNCT
cana-5959	150	5	kleiber	kleiber	PROPN
cana-5959	150	6	,	,	PUNCT
cana-5959	150	7	c.	c.	PROPN
cana-5959	150	8	,	,	PUNCT
cana-5959	150	9	&	&	CCONJ
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cana-5959	150	11	,	,	PUNCT
cana-5959	150	12	s.	s.	PROPN
cana-5959	150	13	(	(	PUNCT
cana-5959	150	14	2008	2008	NUM
cana-5959	150	15	)	)	PUNCT
cana-5959	150	16	.	.	PUNCT
cana-5959	151	1	regression	regression	NOUN
cana-5959	151	2	models	model	NOUN
cana-5959	151	3	for	for	ADP
cana-5959	151	4	count	count	NOUN
cana-5959	151	5	data	datum	NOUN
cana-5959	151	6	in	in	ADP
cana-5959	151	7	r.	r.	PROPN
cana-5959	151	8	journal	journal	PROPN
cana-5959	151	9	of	of	ADP
cana-5959	151	10	statistical	statistical	ADJ
cana-5959	151	11	software	software	NOUN
cana-5959	151	12	,	,	PUNCT
cana-5959	151	13	27(8	27(8	NUM
cana-5959	151	14	)	)	PUNCT
cana-5959	151	15	,	,	PUNCT
cana-5959	151	16	1	1	NUM
cana-5959	151	17	-	-	SYM
cana-5959	151	18	25	25	NUM
cana-5959	151	19	.	.	NOUN
cana-5959	151	20	13	13	NUM
cana-5959	151	21	.	.	PUNCT
cana-5959	152	1	o’hara	o’hara	NOUN
cana-5959	152	2	,	,	PUNCT
cana-5959	152	3	r.b	r.b	PROPN
cana-5959	152	4	.	.	PROPN
cana-5959	152	5	,	,	PUNCT
cana-5959	152	6	&	&	CCONJ
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cana-5959	152	8	,	,	PUNCT
cana-5959	152	9	d.j	d.j	PROPN
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cana-5959	152	12	2010	2010	NUM
cana-5959	152	13	)	)	PUNCT
cana-5959	152	14	.	.	PUNCT
cana-5959	153	1	do	do	AUX
cana-5959	153	2	not	not	PART
cana-5959	153	3	log	log	VERB
cana-5959	153	4	-	-	PUNCT
cana-5959	153	5	transform	transform	NOUN
cana-5959	153	6	count	count	NOUN
cana-5959	153	7	data	datum	NOUN
cana-5959	153	8	.	.	PUNCT
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cana-5959	154	2	in	in	ADP
cana-5959	154	3	ecology	ecology	NOUN
cana-5959	154	4	and	and	CCONJ
cana-5959	154	5	evolution	evolution	NOUN
cana-5959	154	6	,	,	PUNCT
cana-5959	154	7	1(2	1(2	NUM
cana-5959	154	8	)	)	PUNCT
cana-5959	154	9	,	,	PUNCT
cana-5959	154	10	118	118	NUM
cana-5959	154	11	-	-	SYM
cana-5959	154	12	122	122	NUM
cana-5959	154	13	.	.	PUNCT
cana-5959	154	14	14	14	NUM
cana-5959	154	15	.	.	PUNCT
cana-5959	155	1	ver	ver	PROPN
cana-5959	155	2	hoef	hoef	PROPN
cana-5959	155	3	,	,	PUNCT
cana-5959	155	4	j.m	j.m	PROPN
cana-5959	155	5	.	.	PROPN
cana-5959	155	6	,	,	PUNCT
cana-5959	155	7	&	&	CCONJ
cana-5959	155	8	boveng	boveng	PROPN
cana-5959	155	9	,	,	PUNCT
cana-5959	155	10	p.l	p.l	PROPN
cana-5959	155	11	.	.	PROPN
cana-5959	155	12	(	(	PUNCT
cana-5959	155	13	2007	2007	NUM
cana-5959	155	14	)	)	PUNCT
cana-5959	155	15	.	.	PUNCT
cana-5959	156	1	quasi	quasi	ADJ
cana-5959	156	2	-	-	VERB
cana-5959	156	3	poisson	poisson	ADJ
cana-5959	156	4	vs.	vs.	ADP
cana-5959	156	5	negative	negative	ADJ
cana-5959	156	6	binomial	binomial	ADJ
cana-5959	156	7	regression	regression	NOUN
cana-5959	156	8	:	:	PUNCT
cana-5959	156	9	how	how	SCONJ
cana-5959	156	10	should	should	AUX
cana-5959	156	11	we	we	PRON
cana-5959	156	12	model	model	VERB
cana-5959	156	13	overdispersed	overdispersed	ADJ
cana-5959	156	14	count	count	NOUN
cana-5959	156	15	data	datum	NOUN
cana-5959	156	16	?	?	PUNCT
cana-5959	157	1	ecology	ecology	NOUN
cana-5959	157	2	,	,	PUNCT
cana-5959	157	3	88(11	88(11	NUM
cana-5959	157	4	)	)	PUNCT
cana-5959	157	5	,	,	PUNCT
cana-5959	157	6	2766	2766	NUM
cana-5959	157	7	-	-	SYM
cana-5959	157	8	2772	2772	NUM
cana-5959	157	9	.	.	PUNCT
cana-5959	158	1	15	15	NUM
cana-5959	158	2	.	.	X
cana-5959	159	1	hilbe	hilbe	NOUN
cana-5959	159	2	,	,	PUNCT
cana-5959	159	3	j.m	j.m	PROPN
cana-5959	159	4	.	.	PROPN
cana-5959	159	5	(	(	PUNCT
cana-5959	159	6	2014	2014	NUM
cana-5959	159	7	)	)	PUNCT
cana-5959	159	8	.	.	PUNCT
cana-5959	160	1	modeling	model	VERB
cana-5959	160	2	count	count	NOUN
cana-5959	160	3	data	datum	NOUN
cana-5959	160	4	.	.	PUNCT
cana-5959	161	1	cambridge	cambridge	PROPN
cana-5959	161	2	university	university	PROPN
cana-5959	161	3	press	press	NOUN
cana-5959	161	4	.	.	PUNCT