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The exact bootstrap method shown on the example of the mean and variance estimation

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Abstract

The bootstrap method is based on resampling of the original randomsample drawn from a population with an unknown distribution. In the article it was shown that because of the progress in computer technology resampling is actually unnecessary if the sample size is not too large. It is possible to automatically generate all possible resamples and calculate all realizations of the required statistic. The obtained distribution can be used in point or interval estimation of population parameters or in testing hypotheses. We should stress that in the exact bootstrap method the entire space of resamples is used and therefore there is no additional bias which results from resampling. The method was used to estimate mean and variance. The comparison of the obtained distributions with the limit distributions confirmed the accuracy of the exact bootstrap method. In order to compare the exact bootstrap method with the basic method (with random sampling) probability that 1,000 resamples would allow for estimating a parameter with a given accuracy was calculated. There is little chance of obtaining the desired accuracy, which is an argument supporting the use of the exact method. Random sampling may be interpreted as discretization of a continuous variable. Copyright The Author(s) 2013

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  • Joanna Kisielinska, 2013. "The exact bootstrap method shown on the example of the mean and variance estimation," Computational Statistics, Springer, vol. 28(3), pages 1061-1077, June.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:3:p:1061-1077
    DOI: 10.1007/s00180-012-0350-0
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    Cited by:

    1. Dimitris Bertsimas & Bradley Sturt, 2020. "Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms," Operations Research, INFORMS, vol. 68(3), pages 949-964, May.

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