@inproceedings{5d044b15d19443debe51eea58c2b63b2,

title = "Unbiased estimation of the reciprocal mean for non-negative random variables",

abstract = "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.",

author = "Moka, {Sarat Babu} and Kroese, {Dirk P.} and Sandeep Juneja",

year = "2019",

doi = "10.1109/wsc40007.2019.9004815",

language = "English",

isbn = "9781728120522",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "404--415",

editor = "N. Mustafee and Bae, {K.-H. G.} and S. Lazarova-Molnar and M. Rabe and C. Szabo and P. Haas and Y.-J. Son",

booktitle = "Proceedings of the 2019 Winter Simulation Conference",

address = "United States",

note = "2019 Winter Simulation Conference, WSC 2019 ; Conference date: 08-12-2019 Through 11-12-2019",

}