Bounded-influence robust estimation in generalized linear latent variable models

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.