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Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM

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  • Jank, Wolfgang

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  • Jank, Wolfgang, 2005. "Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 685-701, April.
    • Handle: RePEc:eee:csdana:v:48:y:2005:i:4:p:685-701
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    References listed on IDEAS

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    1. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    2. Robert P. Sherman & Yu-Yun K. Ho & Siddhartha R. Dalal, 1999. "Conditions for convergence of Monte Carlo EM sequences with an application to product diffusion modeling," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 248-267.
    3. Pierre L'Ecuyer, 1990. "A Unified View of the IPA, SF, and LR Gradient Estimation Techniques," Management Science, INFORMS, vol. 36(11), pages 1364-1383, November.
    4. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    5. Pierre L’Ecuyer & Christiane Lemieux, 2002. "Recent Advances in Randomized Quasi-Monte Carlo Methods," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 419-474, Springer.
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    Cited by:

    1. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Gonzalez, Jorge & Tuerlinckx, Francis & De Boeck, Paul & Cools, Ronald, 2006. "Numerical integration in logistic-normal models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1535-1548, December.
    3. Theodoros Chatzivasileiadis, 2017. "Quasi-random Monte Carlo application in CGE systematic sensitivity analysis," Papers 1709.09755, arXiv.org.
    4. Chen, Yunxia & Zhang, Wenbo & Xu, Dan, 2019. "Reliability assessment with varying safety threshold for shock resistant systems," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 49-60.
    5. F. Y. Kuo & W. T. M. Dunsmuir & I. H. Sloan & M. P. Wand & R. S. Womersley, 2008. "Quasi-Monte Carlo for Highly Structured Generalised Response Models," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 239-275, June.
    6. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.

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