Simulation-based approach to estimation of latent variable models

Authors:
Zhiguang Qian;Alexander Shapiro
Affiliations:
School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

We propose a simulation-based method for calculating maximum likelihood estimators in latent variable models. The proposed method integrates a recently developed sampling strategy, the so-called Sample Average Approximation method, to efficiently compute high quality solutions of the estimation problem. Theoretical and algorithmic properties of the method are discussed. A computational study, involving two numerical examples, is presented to highlight a significant improvement of the proposed approach over existing methods.