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By O. Capp, E. Moulines and C. P. Robert

Abstract

Abstract While much used in practice, latent variable models raise challenging estimation problems related with the intractability of their likelihoods. Monte Carlo Maximum Likelihood (MCML) is a simulation-based approach to likelihood approximation that has been proposed for complex latent variable models for which deterministic optimization procedures such as the ExpectationMaximization approach are not applicable. It is based on an importance sampling identity for the likelihood ratio, where the importance function is the complete model density at a given parameter value '. This paper studies the asymptotic performance of the MCML method (in the number of observations n) against the choice of ' and of the number of simulations s n use

Topics: Monte Carlo Maximum Likelihood, Simulated Likelihood Ratio, Stochastic Optimization, Stochastic Approximation Running Headline Convergence of Monte
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.147.2919
Provided by: CiteSeerX
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