PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Iterated importance sampling in missing data problems
Gilles Celeux, Jean-Michel Marin and Christian P Robert
Computational Statistics and Dta Analysis 2005.

Abstract

Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking avantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing difficulty, in comparison with existing approaches. The improvement brought by a general Rao-Blackwellisation technique is also discussed.

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EPrint Type:Article
Additional Information:Importance Sampling Methodology
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1852
Deposited By:Gilles Celeux
Deposited On:29 November 2005