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

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 st...

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Bibliographic Details
Published in:Lifetime data analysis 2017-01, Vol.23 (1), p.102-112
Main Author: Chan, Kwun Chuen Gary
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Data analysis
Economics
Finance
Health Sciences
Humans
Image Processing, Computer-Assisted
Insurance
Likelihood Functions
Management
Mathematics and Statistics
Maximum likelihood method
Medicine
Operations Research/Decision Theory
Probability
Quality Control
Regression analysis
Reliability
Safety and Risk
Statistics
Statistics for Business
Statistics for Life Sciences
Studies
Survival analysis
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Summary: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.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-016-9374-z