A multiple-imputation metropolis version of the EM algorithm

• 成果类型: Article
• 署名作者: Gaetan, C; Yao, JF
• 署名单位: University of Padua; Universite de Rennes
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/90.3.643
• 发表日期: 2003
• 页码: 643654
• 关键词: maximum-likelihood; incomplete data; CONVERGENCE

摘要

In this paper we introduce a new stochastic variant of the EM algorithm. The algorithm combines the principle of multiple imputation and the theory of simulated annealing to deal with cases where the E-step and the M-step can be intractable or numerically inefficient.