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Iterated importance sampling in missing data problems

Gilles Celeux 1 Jean-Michel Marin Christian Robert
1 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
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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Submitted on : Friday, May 19, 2006 - 8:36:12 PM
Last modification on : Wednesday, September 16, 2020 - 5:07:08 PM
Long-term archiving on: : Sunday, April 4, 2010 - 9:18:02 PM


  • HAL Id : inria-00070473, version 1



Gilles Celeux, Jean-Michel Marin, Christian Robert. Iterated importance sampling in missing data problems. Computational Statistics and Data Analysis, Elsevier, 2006, 50 (12), pp.3386-3404. ⟨inria-00070473⟩



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