Misspecification in infinite-dimensional Bayesian statistics

• 成果类型: Article
• 署名作者: Kleun, B. J. K.; Van der Wart, A. W.
• 署名单位: Vrije Universiteit Amsterdam
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000029
• 发表日期: 2006
• 页码: 837-877
• 关键词: maximum-likelihood; convergence-rates; distributions; Consistency; BEHAVIOR; SPACES

摘要

We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution P-0, which may not be in the support of the prior, we show that the posterior concentrates its mass near the points in the support of the prior that minimize the Kullback-Leibler divergence with respect to P0. An entropy condition and a prior-mass condition determine the rate of convergence. The method is applied to several examples, with special interest for infinite-dimensional models. These include Gaussian mixtures, nonparametric regression and parametric models.