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Monte Carlo estimation of the density of the sum of dependent random variables

Author

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  • Laub, Patrick J.
  • Salomone, Robert
  • Botev, Zdravko I.

Abstract

We study an unbiased estimator for the density of a sum of random variables that are simulated from a computer model. A numerical study on examples with copula dependence is conducted where the proposed estimator performs favorably in terms of variance compared to other unbiased estimators. We provide applications and extensions to the estimation of marginal densities in Bayesian statistics and to the estimation of the density of sums of random variables under Gaussian copula dependence.

Suggested Citation

  • Laub, Patrick J. & Salomone, Robert & Botev, Zdravko I., 2019. "Monte Carlo estimation of the density of the sum of dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 23-31.
    • Handle: RePEc:eee:matcom:v:161:y:2019:i:c:p:23-31
    DOI: 10.1016/j.matcom.2018.12.001
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    References listed on IDEAS

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    Cited by:

    1. Pierre L’Ecuyer & Florian Puchhammer & Amal Ben Abdellah, 2022. "Monte Carlo and Quasi–Monte Carlo Density Estimation via Conditioning," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1729-1748, May.

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