The dynamic expectation-conditional maximization either' algorithm

• 成果类型: Article
• 署名作者: He, Yunxiao; Liu, Chuanhai
• 署名单位: Purdue University System; Purdue University; Yale University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2011.01013.x
• 发表日期: 2012
• 页码: 313-336
• 关键词: maximum-likelihood-estimation; Latent Variable Models; ecme algorithm; t-distribution; em algorithms; ml-estimation; CONVERGENCE; robust

摘要

. The expectationconditional maximization either (ECME) algorithm has proven to be an effective way of accelerating the expectationmaximization algorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME implementation is what we call DECME-1, which uses the line that is determined by the two most recent estimates as the acceleration subspace. The investigation of DECME-1 leads to an efficient, simple, stable and widely applicable DECME implementation, which uses two-dimensional acceleration subspaces and is referred to as DECME-2. The fast convergence of DECME-2 is established by the theoretical result that, in a small neighbourhood of the maximum likelihood estimate, it is equivalent to a conjugate direction method. The remarkable accelerating effect of DECME-2 and its variant is also demonstrated with several numerical examples.