Date
2011
Lien vers un document non conservé dans cette base
http://arxiv.org/abs/0904.2144v5
Indexation documentaire
Probabilités et mathématiques appliquées
Subject
Metropolis-Hastings algorithm; central limit theorem; Markov Chain Monte Carlo (MCMC); conditioning; variance reduction
Code JEL
C15
Nom de la revue
Annals of Statistics
Volume
39
Numéro
1
Date de publication
2011
Pages article
261-277
Nom de l'éditeur
Institute of Mathematical Statistics
Auteur
Douc, Randal
Robert, Christian P.
Type
Article accepté pour publication ou publié
Résumé en anglais
Casella and Robert (1996) presented a general Rao--Blackwellisation principle for accept-reject and Metropolis-Hastings schemes that leads to significant decreases in the variance of the resulting estimators, but at a high cost in computing and storage. Adopting a completely different perspective, we introduce instead a universal scheme that guarantees variance reductions in all Metropolis-Hastings~based estimators while keeping the computing cost under control. We establish a central limit theorems for the improved estimators and illustrate their performances on toy examples.
