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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