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Sampling algorithms for estimating the mean of bounded random variables

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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.

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Acknowledgment

I would like to thank the anonymous reviewers and Marek J. Druzdzel for their useful comments that considerably improved the presentation of this paper.

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Authors and Affiliations

  1. Decision Systems Laboratory, School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Jian Cheng

Authors
  1. Jian Cheng
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Additional information

This research was supported by the National Science Foundation under Faculty Early Career Development (CAREER) Program, grant IRI-9624629, and by the Air Force Office of Scientific Research under grant number F49620-00-1-0112.

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Cheng, J. Sampling algorithms for estimating the mean of bounded random variables. Computational Statistics 16, 1–23 (2001). https://doi.org/10.1007/s001800100049

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