A recursive algorithm for nonparametric analysis with missing data

• 成果类型: Article
• 署名作者: Newton, MA; Zhang, YL
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/86.1.15
• 发表日期: 1999
• 页码: 1526
• 关键词: dirichlet process prior; bayesian-inference; incomplete data; mixtures; regression; parameter; bootstrap; MODEL

摘要

The mixture of Dirichlet processes posterior that arises in nonparametric Bayesian analysis has been analysed most effectively using Markov chain Monte Carlo. As a computationally simple alternative, we introduce a recursive approximation based on one-step posterior predictive distributions. Asymptotic calculations provide theoretical support for this approximation, and we investigate its actual behaviour in several numerical examples. From a non-Bayesian perspective, this new recursion may be used to obtain solutions of the self-consistency equations.