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Variance Reduction in Monte Carlo Estimators via Empirical Variance Minimization

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Abstract

For Monte Carlo estimators, a variance reduction method based on empirical variance minimization in the class of functions with zero mean (control functions) is described. An upper bound for the efficiency of the method is obtained in terms of the properties of the functional class.

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    D. V. Belomestny, L. S. Iosipoi & N. K. Zhivotovskiy

  2. University of Duisburg-Essen, Duisburg and Essen, Germany

    D. V. Belomestny & N. K. Zhivotovskiy

  3. Faculty of Mechanics and Mathematics, Moscow State University, Moscow, Russia

    L. S. Iosipoi

  4. Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

    N. K. Zhivotovskiy

Authors
  1. D. V. Belomestny
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  2. L. S. Iosipoi
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  3. N. K. Zhivotovskiy
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Corresponding author

Correspondence to L. S. Iosipoi.

Additional information

Original Russian Text © D.V. Belomestny, L.S. Iosipoi, N.K. Zhivotovskiy, 2018, published in Doklady Akademii Nauk, 2018, Vol. 482, No. 6.

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Belomestny, D.V., Iosipoi, L.S. & Zhivotovskiy, N.K. Variance Reduction in Monte Carlo Estimators via Empirical Variance Minimization. Dokl. Math. 98, 494–497 (2018). https://doi.org/10.1134/S1064562418060261

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  • DOI: https://doi.org/10.1134/S1064562418060261

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