Posterior expectation based on empirical likelihoods

• 成果类型: Article
• 署名作者: Vexler, A.; Tao, G.; Hutson, A. D.
• 署名单位: State University of New York (SUNY) System; University at Buffalo, SUNY
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu018
• 发表日期: 2014
• 页码: 711718
• 关键词: approximations

摘要

Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James Stein estimation. We use the Laplace method to establish asymptotic approximations to our proposed posterior expectations, and show by simulation that they are often more efficient than the corresponding classical nonparametric procedures, especially when the underlying data are skewed.