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Latent Variable Regression Analysis with Missing Covariates: Likelihood-Based Methods and Applications - Couverture souple

Xue, Qian Li

 
9783838321578: Latent Variable Regression Analysis with Missing Covariates: Likelihood-Based Methods and Applications
Couverture souple
ISBN 10 : 383832157X ISBN 13 : 9783838321578
Editeur : LAP Lambert Academic Publishing, 2009

Synopsis

Missing data often arises in regression analysis either by study design or stochastic censoring. Restriction of analysis to complete observations may yield biased inferences. Developing likelihood-based methods for analyzing missing data in a regression setting has largely focused on missing values in the dependent variable. In this book, we discuss two likelihood-based approaches to inference for the regression of multivariate categorical outcomes on a set of covariates when some of the covariate values are missing. Specifically, this research seeks to develop methodologies in the context of latent variable models that (i) synthesize multiple outcomes into an latent construct that is easily interpretable yet retains relevant heterogeneity in individual outcomes; (ii) account for measurement inaccuracy in observable outcomes; (iii) model the association between the latent construct and covariates; (iv) handle missing covariate data in both ignorable and nonignorable cases. This book should be of particular interest to psychosocial scientists and others who plan to use latent variables models, but are discouraged by the daunting analytical difficulties associated with missing data.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Missing data often arises in regression analysis either by study design or stochastic censoring. Restriction of analysis to complete observations may yield biased inferences. Developing likelihood-based methods for analyzing missing data in a regression setting has largely focused on missing values in the dependent variable. In this book, we discuss two likelihood-based approaches to inference for the regression of multivariate categorical outcomes on a set of covariates when some of the covariate values are missing. Specifically, this research seeks to develop methodologies in the context of latent variable models that (i) synthesize multiple outcomes into an latent construct that is easily interpretable yet retains relevant heterogeneity in individual outcomes; (ii) account for measurement inaccuracy in observable outcomes; (iii) model the association between the latent construct and covariates; (iv) handle missing covariate data in both ignorable and nonignorable cases. This book should be of particular interest to psychosocial scientists and others who plan to use latent variables models, but are discouraged by the daunting analytical difficulties associated with missing data.

Biographie de l'auteur

Qian-Li Xue, Ph.D.: Studied Biostatistics at the Johns Hopkins University; Assistant Professor of Medicine, Biostatistics at the Johns Hopkins University. Karen Bandeen-Roche, Ph.D.: Studied Operations Research and Industrial Engineering at Cornell University; Hurley Dorrier Professor and Chair of Biostatistics at the Johns Hopkins University.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP Lambert Academic Publishing
Date d'édition
2009
Langue
anglais
ISBN 10 
383832157X
ISBN 13 
9783838321578
Reliure
Broché
Nombre de pages
148
Coordonnées du fabricant
non disponible
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