Latent variable models with fixed effects

Sammel, MD; Ryan, LM

Latent variable models with fixed effects

Sammel, MD Ryan, LM

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Publication Type:: Journal Article
Citation:: Biometrics, 1996, 52 (2), pp. 650 - 663
Issue Date:: 1996-06-01

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Field	Value	Language
dc.contributor.author	Sammel, MD	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	1996-06-01	en_US
dc.identifier.citation	Biometrics, 1996, 52 (2), pp. 650 - 663	en_US
dc.identifier.issn	0006-341X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26192
dc.description.abstract	We discuss latent variable models that allow for fixed effect covariates, as well as covariates affecting the latent variable directly. Restricted maximum likelihood and maximum likelihood are used to estimate model parameters. A generalized likelihood ratio test can be used to test significance of the covariates effecting the latent outcomes. Special cases of the proposed model correspond to factor analysis, mixed models, random effects models, and simultaneous equations. The model is applied to birth defects data, where continuous data on the size of infants who were exposed to anticonvulsant medications in utero are compared to controls.	en_US
dc.relation.ispartof	Biometrics	en_US
dc.relation.isbasedon	10.2307/2532903	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Fetal Growth Retardation	en_US
dc.subject.mesh	Anticonvulsants	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Likelihood Functions	en_US
dc.subject.mesh	Prospective Studies	en_US
dc.subject.mesh	Biometry	en_US
dc.subject.mesh	Pregnancy	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Infant, Newborn	en_US
dc.subject.mesh	Outcome Assessment (Health Care)	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Outcome Assessment, Health Care	en_US
dc.title	Latent variable models with fixed effects	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	52	en_US
utslib.for	010402 Biostatistics	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

We discuss latent variable models that allow for fixed effect covariates, as well as covariates affecting the latent variable directly. Restricted maximum likelihood and maximum likelihood are used to estimate model parameters. A generalized likelihood ratio test can be used to test significance of the covariates effecting the latent outcomes. Special cases of the proposed model correspond to factor analysis, mixed models, random effects models, and simultaneous equations. The model is applied to birth defects data, where continuous data on the size of infants who were exposed to anticonvulsant medications in utero are compared to controls.

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

http://hdl.handle.net/10453/26192