Iterated importance sampling in missing data problems
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
TypeArticle accepté pour publication ou publié
Journal nameComputational Statistics and Data Analysis
MetadataShow full item record
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 / KeywordsAdaptive algorithms; Bayesian inference; Latent variable models; Population Monte Carlo; Rao–Blackwellisation; Stochastic volatility model
Showing items related by title and author.
Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation Celeux, Gilles; El Anbari, Mohammed; Marin, Jean-Michel; Robert, Christian P. (2012) Article accepté pour publication ou publié
Some discussions on the Read Paper Beyond subjective and objective in statistics" by A. Gelman and C. Hennig" Celeux, Gilles; Jewson, Jack; Josse, Julie; Marin, Jean-Michel; Robert, Christian P. (2017) Document de travail / Working paper