Abstract #300213

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JSM 2003 Abstract #300213
Activity Number: 214
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #300213
Title: Latent Variable Modeling with Mixed Continuous and Polytomous Data
Author(s): Jens C. Eickhoff*+ and Yasuo Amemiya
Companies: University of Wisconsin, Madison and IBM T. J. Watson Research Center
Address: 238 WARF Building, 610 Walnut St., Madison, WI, 53726,
Keywords: structural equation models ; pseudolikelihood estimation ; mixed outcome variables
Abstract:

Latent variable models are commonly used to perform causal analysis in observational epidemiological studies. The models used in such analysis relate all observed variables to latent common factors. In many epidemiological studies, however, some or all outcome variables are in polytomous form. The existing approaches for latent variable models with polytomous outcomes can be considered lacking in several aspects. We propose a computationally convenient pseudolikelihood approach for latent variable models with mixed continuous and polytomous variables. Asymptotic properties of the estimators are derived. Simulation studies are conducted to examine the empirical behavior and to compare it with existing methods. The method is illustrated with a real example.

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