On stochastic versions of the em algorithm

Ian C. Marschner*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

A previously proposed stochastic modification of the EM algorithm is discussed, in which an intractable E-step is replaced by a single simulation of the complete data, followed by averaging of the resulting Markov chain iterative sequence. A connection is drawn between this approach and a modified EM algorithm in which the E- and M-steps are carried out in reverse order. Since this modified EM algorithm is equivalent to solving a biased estimating equation in finite samples, a simple modification of the stochastic EM algorithm is suggested. The modified stochastic algorithm is applicable when the E-step of an EM algorithm is intractable, and it is related to a deterministic algorithm that solves an unbiased estimating equation. In small-sample simulation studies of standard censoring and mixture problems, the modified stochastic algorithm outperforms the usual stochastic EM algorithm and the maximum likelihood estimator. In large samples all approaches perform similarly.

Original languageEnglish
Pages (from-to)281-286
Number of pages6
JournalBiometrika
Volume88
Issue number1
Publication statusPublished - 2001
Externally publishedYes

Keywords

  • Em algorithm
  • Estimating equation
  • Stochastic algorithm

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