Abstract #301654

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JSM 2002 Abstract #301654
Activity Number: 176
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section*
Abstract - #301654
Title: Pseudo Likelihood Approach for Nonlinear and Non-normal Structural Equation Analysis
Author(s): Yan Zhao*+ and Yasuo Amemiya
Affiliation(s): Iowa State University and IBM T. J. Watson Research Center
Address: 117 Snedecor Hall, Ames, Iowa, 50011, USA
Keywords: MCEM algorithm ; normal mixture ; standard error estimation ; deconvolution ; bootstrap ; latent variable modeling
Abstract:

Structural equation analysis is widely used in economics and social sciences. The model considered in this paper consists of two parts: a linear measurement model relating observed measurements to underlying latent variables, and a nonlinear structural model representing relationships among the latent variables. When the distributional form of the latent variables is unspecified, a pseudo likelihood approach, based on a hypothetical normal mixture assumption, is proposed. To obtain the pseudo likelihood parameter estimates, the Monte Carlo EM algorithm is developed. Standard error estimates for the estimated structural parameters are obtained combining an empirical observed information estimates and a bootstrap estimated covariance matrix for the nuisance parameters. Simulation studies are reported.

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