Unbiased estimation of the reciprocal mean for non-negative random variables

Sarat Babu Moka, Dirk P. Kroese, Sandeep Juneja

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


In recent years, Monte Carlo estimators have been proposed that can estimate the ratio of two expectations without bias. We investigate the theoretical properties of a Taylor-expansion based estimator of the reciprocal mean of a non-negative random variable. We establish explicit expressions for the computational efficiency of this estimator and obtain optimal choices for its parameters. We also derive corresponding practical confidence intervals and show that they are asymptotically equivalent to the maximum likelihood (biased) ratio estimator as the simulation budget increases.

Original languageEnglish
Title of host publicationProceedings of the 2019 Winter Simulation Conference
EditorsN. Mustafee, K.-H. G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, Y.-J. Son
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages12
ISBN (Electronic)9781728132839, 9781728132822
ISBN (Print)9781728120522
Publication statusPublished - 2019
Externally publishedYes
Event2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States
Duration: 8 Dec 201911 Dec 2019

Publication series

ISSN (Print)0891-7736
ISSN (Electronic)1558-4305


Conference2019 Winter Simulation Conference, WSC 2019
Country/TerritoryUnited States
CityNational Harbor


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