General Departmental Seminar Series
Selecting the Number of Classes under Latent Class Regression Models: A Factor Analysis Analogous Approach
Guan-Hua Huang, Departments of Preventive Medicine
and Biostatistics and Medical Informatics,
University of Wisconsin
Friday, Mar 9, 2001, 12:00-1:00 p.m.
3285 Medical Sciences Center - 1300 University Ave.
Recently, the latent class regression (LCR) model has received much attention in the field of medical research. The basic LCR model summarizes shared features of measured multiple indicators as an underlying categorical variable and incorporates the covariate information in modeling both latent class membership and the multiple indicators themselves. To reduce complexity and enhance interpretability, one usually fixes the number of classes in a given LCR. Often, goodness of fit methods comparing various estimated models are used as a criterion to select the number of classes. In this talk, I propose a new method which is based on an analogous method used in factor analysis and does not require repeated fitting. Two ideas with application to many settings other than ours are synthesized in deriving the method: a connection between latent class models and factor analysis, and techniques of covariate marginalization and elimination. A Monte Carlo simulation study is presented to evaluate the behavior of the selection procedure.
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