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Monte Carlo integration with a growing number of control variates

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  • Portier, Francois
  • Segers, Johan

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  • Portier, Francois & Segers, Johan, 2018. "Monte Carlo integration with a growing number of control variates," LIDAM Discussion Papers ISBA 2018001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2018001
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    File URL: https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A195210/datastream/PDF_01/view
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    References listed on IDEAS

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    1. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    3. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, 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.
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    Cited by:

    1. Fengpei Li & Haoxian Chen & Jiahe Lin & Arkin Gupta & Xiaowei Tan & Honglei Zhao & Gang Xu & Yuriy Nevmyvaka & Agostino Capponi & Henry Lam, 2024. "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate," Papers 2412.11257, arXiv.org, revised Jun 2025.
    2. Ke-Lin Du & Rengong Zhang & Bingchun Jiang & Jie Zeng & Jiabin Lu, 2025. "Understanding Machine Learning Principles: Learning, Inference, Generalization, and Computational Learning Theory," Mathematics, MDPI, vol. 13(3), pages 1-56, January.

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