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English

Bounded-Influence Robust Estimation in Generalized Linear Latent Variable Models

Contributors

Moustaki, Irini; Victoria-Feser, Maria-Pia

Published in

Journal of the American Statistical Association, vol. 101, no. 474, p. 644-653

Publication date

2006

Abstract

Latent variable models are used for analyzing multivariate data. Recently, generalized linear latent variable models for categorical, metric, and mixed-type responses estimated via maximum likelihood (ML) have been proposed. Model deviations, such as data contamination, are shown analytically, using the influence function and through a simulation study, to seriously affect ML estimation. This article proposes a robust estimator that is made consistent using the basic principle of indirect inference and can be easily numerically implemented. The performance of the robust estimator is significantly better than that of the ML estimators in terms of both bias and variance. A real example from a consumption survey is used to highlight the consequences in practice of the choice of the estimator.

Keywords

  • Indirect inference
  • Influence function
  • Latent variable models
  • Mixed variables
  • Robust estimation

UNIGE affiliation entities

Citation (ISO format)

MOUSTAKI, Irini, VICTORIA-FESER, Maria-Pia. Bounded-Influence Robust Estimation in Generalized Linear Latent Variable Models. In: Journal of the American Statistical Association, 2006, vol. 101, n° 474, p. 644–653. doi: 10.1198/016214505000001320

Main files (1)

Article (Accepted version)

Identifiers

Journal ISSN

0162-1459
920views
1581downloads

Technical informations

Creation

30/04/2010 12:27:00

First validation

30/04/2010 12:27:00

Update

14/03/2023 15:28:36

Status update

14/03/2023 15:28:36

Last indexation

29/10/2024 14:43:04
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