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Latent Variable Regression Analysis with Missing Covariates: Likelihood-Based Methods and Applications - Softcover
Inhaltsangabe
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.
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Reseña del editor
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.
Biografía del autor
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.
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- VerlagLAP Lambert Academic Publishing
- Erscheinungsdatum2009
- ISBN 10 383832157X
- ISBN 13 9783838321578
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten148
- Kontakt zum HerstellerNicht verfügbar
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Latent Variable Regression Analysis with Missing Covariates
Verlag:
LAP LAMBERT Academic Publishing Okt 2009, 2009
ISBN 10: 383832157X
ISBN 13: 9783838321578
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Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 148 pp. Englisch. Artikel-Nr. 9783838321578
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