A fast score test for generalized mixture models.

Duan, R; Ning, Y; Wang, S; Lindsay, BG; Carroll, RJ; Chen, Y

A fast score test for generalized mixture models.

Duan, R Ning, Y Wang, S Lindsay, BG Carroll, RJ Chen, Y

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Biometrics, 2020, 76, (3), pp. 811-820
Issue Date:: 2020-09

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Field	Value	Language
dc.contributor.author	Duan, R
dc.contributor.author	Ning, Y
dc.contributor.author	Wang, S
dc.contributor.author	Lindsay, BG
dc.contributor.author	Carroll, RJ
dc.contributor.author	Chen, Y
dc.date.accessioned	2021-03-31T08:54:46Z
dc.date.available	2019-12-04
dc.date.available	2021-03-31T08:54:46Z
dc.date.issued	2020-09
dc.identifier.citation	Biometrics, 2020, 76, (3), pp. 811-820
dc.identifier.issn	0006-341X
dc.identifier.issn	1541-0420
dc.identifier.uri	http://hdl.handle.net/10453/147728
dc.description.abstract	In biomedical studies, testing for homogeneity between two groups, where one group is modeled by mixture models, is often of great interest. This paper considers the semiparametric exponential family mixture model proposed by Hong et al. (2017) and studies the score test for homogeneity under this model. The score test is nonregular in the sense that nuisance parameters disappear under the null hypothesis. To address this difficulty, we propose a modification of the score test, so that the resulting test enjoys the Wilks phenomenon. In finite samples, we show that with fixed nuisance parameters the score test is locally most powerful. In large samples, we establish the asymptotic power functions under two types of local alternative hypotheses. Our simulation studies illustrate that the proposed score test is powerful and computationally fast. We apply the proposed score test to an UK ovarian cancer DNA methylation data for identification of differentially methylated CpG sites.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	WILEY
dc.relation	National Cancer InstituteU01CA057030
dc.relation.ispartof	Biometrics
dc.relation.isbasedon	10.1111/biom.13204
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 0199 Other Mathematical Sciences
dc.subject.classification	Statistics & Probability
dc.title	A fast score test for generalized mixture models.
dc.type	Journal Article
utslib.citation.volume	76
utslib.location.activity	United States
utslib.for	0104 Statistics
utslib.for	0104 Statistics
utslib.for	0199 Other Mathematical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-31T08:54:45Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	76
utslib.citation.issue	3

Abstract:

In biomedical studies, testing for homogeneity between two groups, where one group is modeled by mixture models, is often of great interest. This paper considers the semiparametric exponential family mixture model proposed by Hong et al. (2017) and studies the score test for homogeneity under this model. The score test is nonregular in the sense that nuisance parameters disappear under the null hypothesis. To address this difficulty, we propose a modification of the score test, so that the resulting test enjoys the Wilks phenomenon. In finite samples, we show that with fixed nuisance parameters the score test is locally most powerful. In large samples, we establish the asymptotic power functions under two types of local alternative hypotheses. Our simulation studies illustrate that the proposed score test is powerful and computationally fast. We apply the proposed score test to an UK ovarian cancer DNA methylation data for identification of differentially methylated CpG sites.

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http://hdl.handle.net/10453/147728