Acceleration of Expectation-Maximization algorithm for length-biased right-censored data
Kwun Chuen Gary Chan ()
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Kwun Chuen Gary Chan: University of Washington
Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 1, No 6, 102-112
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.
Keywords: Aitken’s delta squared; Expectation-Maximization; Iterative convex minorant; Isotonic regression; Multiplicative censoring (search for similar items in EconPapers)
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