Minimum $$\phi $$ ϕ -Divergence Estimation in Constrained Latent Class Models for Binary Data

A. Felipe, P. Miranda () and L. Pardo

Psychometrika, 2015, vol. 80, issue 4, 1020-1042

Abstract: The main purpose of this paper is to introduce and study the behavior of minimum $$\phi $$ ϕ -divergence estimators as an alternative to the maximum-likelihood estimator in latent class models for binary items. As it will become clear below, minimum $$\phi $$ ϕ -divergence estimators are a natural extension of the maximum-likelihood estimator. The asymptotic properties of minimum $$\phi $$ ϕ -divergence estimators for latent class models for binary data are developed. Finally, to compare the efficiency and robustness of these new estimators with that obtained through maximum likelihood when the sample size is not big enough to apply the asymptotic results, we have carried out a simulation study. Copyright The Psychometric Society 2015

Keywords: latent class models; minimum phi-divergence estimator; maximum-likelihood estimator; asymptotic distribution (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11336-015-9450-4

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