Faster estimates of the mean of bounded random variables

Mark Huber and Bo Jones

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 161, issue C, 93-101

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.

Keywords: Adaptive Monte Carlo; Robust estimates; Randomized approximation scheme (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:161:y:2019:i:c:p:93-101

DOI: 10.1016/j.matcom.2019.01.011

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