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A multiple-imputation Metropolis version of the EM algorithm

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  • Carlo Gaetan

Abstract

In this paper we introduce a new stochastic variant of the EM algorithm. The algorithm combines the principle of multiple imputation and the theory of simulated annealing to deal with cases where the E-step and the M-step can be intractable or numerically inefficient. Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • Carlo Gaetan, 2003. "A multiple-imputation Metropolis version of the EM algorithm," Biometrika, Biometrika Trust, vol. 90(3), pages 643-654, September.
    • Handle: RePEc:oup:biomet:v:90:y:2003:i:3:p:643-654
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    Cited by:

    1. Augustyniak, Maciej, 2014. "Maximum likelihood estimation of the Markov-switching GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 61-75.
    2. Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    3. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    4. Christian P. Robert, 2014. "Discussion," International Statistical Review, International Statistical Institute, vol. 82(1), pages 79-81, April.

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