Simulation-based approach to estimation of latent variable models

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.