Stochastic EM

Publikation: Bidrag til bog/antologi/rapportEncyclopædiartikelForskning

Abstrakt

The expectation maximization (EM) algorithm is a useful tool for finding the maximum likelihood estimator (MLE) in incomplete data problems. In some problems, however, the E step (and/or the M step) of the algorithm may be difficult to implement. Here, the stochastic EM algorithm can provide a useful alternative by replacing the E step of the EM algorithm with a fixed number of simulations, turning the M step into a maximization of the complete data log‐likelihood. The output of the stochastic EM algorithm forms a Markov chain that under sufficient regularity conditions is ergodic with an asymptotically normal invariant distribution. Draws from the invariant distribution form a consistent asymptotically normal estimator of the unknown parameters.
OriginalsprogEngelsk
TitelWiley StatsRef : Statistics Reference Online
RedaktørerMarie Davidian, Ron S. Kenett, Nicholas T. Longford, Geert Molenberghs, Walter Piegorsch, Fabrizio Ruggeri
Udgivelses stedHoboken, NJ
ForlagWiley
Publikationsdato15 maj 2018
ISBN (Elektronisk)9781118445112
DOI
StatusUdgivet - 15 maj 2018

Bibliografisk note

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Emneord

  • Incomplete data
  • EM algorithm
  • Imputation
  • Simulation
  • Estimation

Citationsformater

Nielsen, S. F. (2018). Stochastic EM. I M. Davidian, R. S. Kenett, N. T. Longford, G. Molenberghs, W. Piegorsch, & F. Ruggeri (red.), Wiley StatsRef: Statistics Reference Online Hoboken, NJ: Wiley. https://doi.org/10.1002/9781118445112.stat08121