Pseudo-likelihood inference for clustered binary data

Geys, H; Molenberghs, G; Ryan, LM

Pseudo-likelihood inference for clustered binary data

Geys, H Molenberghs, G Ryan, LM

Permalink

Publication Type:: Journal Article
Citation:: Communications in Statistics - Theory and Methods, 1997, 26 (11), pp. 2743 - 2767
Issue Date:: 1997-01-01

Closed Access

	Filename	Description	Size
	2011008594OK.pdf		2.93 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Geys, H	en_US
dc.contributor.author	Molenberghs, G	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	1997-01-01	en_US
dc.identifier.citation	Communications in Statistics - Theory and Methods, 1997, 26 (11), pp. 2743 - 2767	en_US
dc.identifier.issn	0361-0926	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26188
dc.description.abstract	Molenberghs and Ryan (1996) proposed a likelihood-based model for clustered binary data, based on a multivariate exponential family model (Cox, 1972). The model benefits from the elegance and simplicity of exponential family theory and is flexible in terms of allowing response rates to depend on cluster size. A main problem however, particularly with large clusters is the evaluation of the normalizing constant. In this paper, pseudo-likelihood is explored as an alternative mode of inference. The pseudo-likelihood equations are derived, the model is applied to data from a developmental toxicity study, and an asymptotic and small sample relative efficiency study is performed.	en_US
dc.relation.ispartof	Communications in Statistics - Theory and Methods	en_US
dc.relation.isbasedon	10.1080/03610929708832075	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Pseudo-likelihood inference for clustered binary data	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	26	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
pubs.embargo.period	Not known	en_US
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
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

Molenberghs and Ryan (1996) proposed a likelihood-based model for clustered binary data, based on a multivariate exponential family model (Cox, 1972). The model benefits from the elegance and simplicity of exponential family theory and is flexible in terms of allowing response rates to depend on cluster size. A main problem however, particularly with large clusters is the evaluation of the normalizing constant. In this paper, pseudo-likelihood is explored as an alternative mode of inference. The pseudo-likelihood equations are derived, the model is applied to data from a developmental toxicity study, and an asymptotic and small sample relative efficiency study is performed.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26188