Iterated importance sampling in missing data problems

Celeux, Gilles; Marin, Jean-Michel; Robert, Christian P.

Celeux, Gilles; Marin, Jean-Michel; Robert, Christian P. (2006), Iterated importance sampling in missing data problems, Computational Statistics and Data Analysis, 50, 12, p. 3386-3404. http://dx.doi.org/10.1016/j.csda.2005.07.018

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Type

Article accepté pour publication ou publié

Date

2006

Journal name

Computational Statistics and Data Analysis

Volume

Number

Publisher

Elsevier

Pages

3386-3404

Abstract (EN)

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 advantage 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.

Subjects / Keywords

Adaptive algorithms; Bayesian inference; Latent variable models; Population Monte Carlo; Rao–Blackwellisation; Stochastic volatility model

JEL

C15 - Statistical Simulation Methods: General