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

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Abstract

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.

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Acknowledgments

The author is partially funded by the National Institute of Health Grant R01 HL122212.

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Authors and Affiliations

  1. Department of Biostatistics, University of Washington, Campus, Box 357232, Seattle, WA, 98195-7232, USA

    Kwun Chuen Gary Chan

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  1. Kwun Chuen Gary Chan
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Correspondence to Kwun Chuen Gary Chan.

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Chan, K.C.G. Acceleration of Expectation-Maximization algorithm for length-biased right-censored data. Lifetime Data Anal 23, 102–112 (2017). https://doi.org/10.1007/s10985-016-9374-z

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  • DOI: https://doi.org/10.1007/s10985-016-9374-z

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