Sampling algorithms for estimating the mean of bounded random variables

Jian Cheng
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Jian Cheng: University of Pittsburgh

Computational Statistics, 2001, vol. 16, issue 1, No 1, 23 pages

Abstract: Summary We show two distribution-independent algorithms to estimate the mean of bounded random variables, one with the knowledge of variance, the other without. These algorithms guarantee that the estimate is within the desired precision with an error probability less than or equal to the requirement. Some simplified stopping rules are also given.

Keywords: Algorithm; Sampling; Monte Carlo estimation; Confidence intervals (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1007/s001800100049

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