Random Effect and Latent Variable Model Selection
Publication year: 2008
ISBN: 978-0-387-76721-5
Internet Resource: Please Login to download book
Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings. This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers.
Subject: Mathematics and Statistics, Factor analysis, Generalized linear model, Latent variable model, Latent variables, Likelihood, Variance, model selection, random effects, structural equation models, subset selection, variable selection
