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Large-sample variance of simulation using refined descriptive sampling: Case of independent variables

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  • Leila Baiche
  • Megdouda Ourbih-Tari

Abstract

Derived from descriptive sampling (DS) as a better approach to Monte Carlo simulation, refined DS is a method of sampling that can be used to produce input values for estimation of expectations of functions of output variables. In this article, the asymptotic variance of such an estimate in case of independent input variables is obtained and it was shown that asymptotically, the variance is less than that obtained using simple random sampling.

Suggested Citation

  • Leila Baiche & Megdouda Ourbih-Tari, 2017. "Large-sample variance of simulation using refined descriptive sampling: Case of independent variables," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(1), pages 510-519, January.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:1:p:510-519
    DOI: 10.1080/03610926.2014.997362
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    Cited by:

    1. Oualid Saci & Megdouda Ourbih-Tari & Leila Baiche, 2023. "Maximum Likelihood Estimation of Parameters of a Random Variable Using Monte Carlo Methods," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 540-571, February.
    2. Tamiti Kenza & Ourbih-Tari Megdouda & Aloui Abdelouhab & Idjis Khelidja, 2018. "The use of variance reduction, relative error and bias in testing the performance of M/G/1 retrial queues estimators in Monte Carlo simulation," Monte Carlo Methods and Applications, De Gruyter, vol. 24(3), pages 165-178, September.
    3. Boubalou Meriem & Ourbih-Tari Megdouda & Aloui Abdelouhab & Zioui Arezki, 2019. "Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction," Monte Carlo Methods and Applications, De Gruyter, vol. 25(2), pages 177-186, June.

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