Minimum ϕ -Divergence Estimation in Constrained Latent Class Models for Binary Data.
The main purpose of this paper is to introduce and study the behavior of minimum 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 ϕ -divergence estimators are a natural extension of the maximum-likelihood estimator. The asymptotic properties of minimum 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.
Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, 28040 , Madrid, Spain.
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Felipe
Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, 28040 , Madrid, Spain. pmiranda@mat.ucm.es.
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Miranda
Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, 28040 , Madrid, Spain.
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Pardo
D016013:Likelihood Functions; D015233:Models, Statistical; D011594:Psychometrics / Q000706:statistics & numerical data
asymptotic distribution; latent class models; maximum-likelihood estimator; minimum phi-divergence estimator