Le lundi 15 mars 2010 à 15:00 - UM2 - Salle SC16.01, bâtiment 16Mathieu Ribatet
With Daniel Cooley (EPFL) and Anthony C. Davison (Colorado State University) Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although the maximum composite likelihood estimator has frequentist properties akin to those of the usual maximum likelihood estimator, Bayesian inference based on composite likelihoods has yet to be explored. In this paper we investigate the use of the Metropolis-Hastings algorithm to compute a pseudo-posterior distribution based on the composite likelihood. Two methodologies for adjusting the algorithm are presented and their performance on approximating the true posterior distribution is investigated using simulated data sets and real data on spatial extremes of rainfall. Keywords: Bayesian hierarchical model; Composite likelihood, Markov chain Monte Carlo; Metropolis-Hastings algorithm; max-stable process; Rainfall data.