Acceleration of Expectation-Maximization algorithm for length-biased right-censored data

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• 作者：Kwun Chuen Gary Chan
• 关键词：Aitken’s delta squared ; Expectation ; Maximization ; Iterative convex minorant ; Isotonic regression ; Multiplicative censoring
• 刊名：Lifetime Data Analysis
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：23
• 期：1
• 页码：102-112
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Statistics for Life Sciences, Medicine, Health Sciences; Quality Control, Reliability, Safety and Risk; Statistics for Business/Economics/Mathematical Finance/Insurance; Operation
• 出版者：Springer US
• ISSN：1572-9249
• 卷排序：23

文摘

Vardi’s Expectation-Maximization (EM) algorithm is frequently used for computing the nonparametric maximum likelihood estimator of length-biased right-censored data, which does not admit a closed-form representation. The EM algorithm may converge slowly, particularly for heavily censored data. We studied two algorithms for accelerating the convergence of the EM algorithm, based on iterative convex minorant and Aitken’s delta squared process. Numerical simulations demonstrate that the acceleration algorithms converge more rapidly than the EM algorithm in terms of number of iterations and actual timing. The acceleration method based on a modification of Aitken’s delta squared performed the best under a variety of settings.