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Article: Multiple regression analysis of twin data: A model-fitting approach

TitleMultiple regression analysis of twin data: A model-fitting approach
Authors
Keywordsheritability
model-fitting
multiple regression
twins
Issue Date1992
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244
Citation
Behavior Genetics, 1992, v. 22 n. 4, p. 489-497 How to Cite?
AbstractThe multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting the h2 and c2 terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.
Persistent Identifierhttp://hdl.handle.net/10722/143634
ISSN
2015 Impact Factor: 3.268
2015 SCImago Journal Rankings: 1.457
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCherny, SSen_HK
dc.contributor.authorDeFries, JCen_HK
dc.contributor.authorFulker, DWen_HK
dc.date.accessioned2011-12-16T08:08:19Z-
dc.date.available2011-12-16T08:08:19Z-
dc.date.issued1992en_HK
dc.identifier.citationBehavior Genetics, 1992, v. 22 n. 4, p. 489-497en_HK
dc.identifier.issn0001-8244en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143634-
dc.description.abstractThe multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting the h2 and c2 terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.en_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244en_HK
dc.relation.ispartofBehavior Geneticsen_HK
dc.subjectheritabilityen_HK
dc.subjectmodel-fittingen_HK
dc.subjectmultiple regressionen_HK
dc.subjecttwinsen_HK
dc.titleMultiple regression analysis of twin data: A model-fitting approachen_HK
dc.typeArticleen_HK
dc.identifier.emailCherny, SS: cherny@hku.hken_HK
dc.identifier.authorityCherny, SS=rp00232en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/BF01066617en_HK
dc.identifier.pmid1503550-
dc.identifier.scopuseid_2-s2.0-0026622871en_HK
dc.identifier.volume22en_HK
dc.identifier.issue4en_HK
dc.identifier.spage489en_HK
dc.identifier.epage497en_HK
dc.identifier.isiWOS:A1992JE57600006-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCherny, SS=7004670001en_HK
dc.identifier.scopusauthoridDeFries, JC=7005658115en_HK
dc.identifier.scopusauthoridFulker, DW=7005792286en_HK
dc.identifier.citeulike4325490-

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