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Faster estimates of the mean of bounded random variables

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  • Huber, Mark
  • Jones, Bo

Abstract

This work presents a new adaptive algorithm for robustly estimating the mean of a bounded random variable with unknown variance. Previous algorithms came within a constant factor of the best possible average number of samples for this problem, but the constant was large enough to discourage use. Here we present an algorithm that uses at most 40% as many samples on average as the previous approach, and runs as quickly as Central Limit Theorem based heuristic estimates (to first order) on a large class of problems. The method is illustrated using a network reliability problem and importance sampling.

Suggested Citation

  • Huber, Mark & Jones, Bo, 2019. "Faster estimates of the mean of bounded random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 93-101.
    • Handle: RePEc:eee:matcom:v:161:y:2019:i:c:p:93-101
    DOI: 10.1016/j.matcom.2019.01.011
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    References listed on IDEAS

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    1. Pierre L’Ecuyer & Bruno Tuffin, 2011. "Approximating zero-variance importance sampling in a reliability setting," Annals of Operations Research, Springer, vol. 189(1), pages 277-297, September.
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