Frequentist accuracy of Bayesian estimates

• 成果类型: Article
• 署名作者: Efron, Bradley
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12080
• 发表日期: 2015
• 页码: 617-646
• 关键词: selection; inference; priors

摘要

In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of uninformative' prior distribution intended to have minimal inferential influence. The Bayes rule will still produce nice looking estimates and credible intervals, but these lack the logical force that is attached to experience-based priors and require further justification. The paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate. The same simulations as required for the point estimate also produce the standard deviation. Exponential family models make the calculations particularly simple and bring in a connection to the parametric bootstrap.