Bernoulli

  • Bernoulli
  • Volume 6, Number 3 (2000), 457-489.

The stochastic EM algorithm: estimation and asymptotic results

Søren Feodor Nielsen

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Abstract

The EM algorithm is a much used tool for maximum likelihood estimation in missing or incomplete data problems. However, calculating the conditional expectation required in the E-step of the algorithm may be infeasible, especially when this expectation is a large sum or a high-dimensional integral. Instead the expectation can be estimated by simulation. This is the common idea in the stochastic EM algorithm and the Monte Carlo EM algorithm.

Article information

Source
Bernoulli, Volume 6, Number 3 (2000), 457-489.

Dates
First available in Project Euclid: 10 April 2004

Permanent link to this document
https://projecteuclid.org/euclid.bj/1081616701

Mathematical Reviews number (MathSciNet)
MR2001f:62016

Zentralblatt MATH identifier
0981.62022

Keywords
EM algorithm incomplete observations simulation

Citation

Feodor Nielsen, Søren. The stochastic EM algorithm: estimation and asymptotic results. Bernoulli 6 (2000), no. 3, 457--489. https://projecteuclid.org/euclid.bj/1081616701


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