Abstract #301264

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JSM 2003 Abstract #301264
Activity Number: 248
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #301264
Title: Estimation of Generalized Linear Latent Variable Models
Author(s): Philippe Huber*+ and Elvezio Ronchetti and Maria-Pia Victoria-Feser
Companies: University of Geneva and Universite de Geneve and University of Geneva
Address: Department of Econometrics, Geneva, 1211, Switzerland
Keywords: ordinal data ; factor analysis ; M-estimators ; Laplace approximation
Abstract:

Generalized Linear Latent Variable Models (GLLVM) allow to model relationships between manifest and latent variables when the manifest variables are of various type, such as continuous or discrete. They extend structural equation modelling techniques which are very powerful modeling tools in the social sciences. The log-likelihood of GLLVM cannot be written explicitly and numerical integration is often used to carry out estimation and inference. Instead of a Gauss-Hermite Quadrature, we propose to use a Laplace approximation. This leads to the expression of a new estimator for the parameters of a GLLVM that can be viewed as a $M$-estimator leading to readily available asymptotic properties and correct inference. A practical advantage of our estimator is that it can be computed even for models with a large number of variables. Simulation studies in various settings show its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL.

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