Convergence rates for posterior distributions and adaptive estimation

• 成果类型: Article
• 署名作者: Huang, TM
• 署名单位: Iowa State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000490
• 发表日期: 2004
• 页码: 1556-1593
• 关键词: model selection; Consistency; inference

摘要

The goal of this paper is to provide theorems on convergence rates of posterior distributions that can be applied to obtain good convergence rates in the context of density estimation as well as regression. We show how to choose priors so that the posterior distributions converge at the optimal rate without prior knowledge of the degree of smoothness of the density function or the regression function to be estimated.