HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/inria-00070473
Contributor : Rapport de Recherche Inria Connect in order to contact the contributor
Submitted on : Friday, May 19, 2006 - 8:36:12 PM
Last modification on : Wednesday, April 20, 2022 - 3:37:31 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:18:02 PM

Identifiers

  • HAL Id : inria-00070473, version 1

Citation

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⟩

Share

Metrics

Record views

115

Files downloads

222