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Computing empirical likelihood from the bootstrap

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  • Pawitan, Yudi

Abstract

The close relationship between the bootstrap and empirical likelihood has been noted in the literature. The purpose of this paper is to show how to construct a bootstrap likelihood from a single bootstrap, without any nested bootstrapping nor any smoothing. For a wide class of M-estimators the likelihood agrees with the empirical likelihood up to order O(n-1/2). The resulting likelihood may be used for display purpose, for computing likelihood-based confidence intervals or for future use in combining information.

Suggested Citation

  • Pawitan, Yudi, 2000. "Computing empirical likelihood from the bootstrap," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 337-345, May.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:4:p:337-345
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    Cited by:

    1. Bruce Worton, 2012. "Efficient construction of a smooth nonparametric family of empirical distributions and calculation of bootstrap likelihood," Computational Statistics, Springer, vol. 27(2), pages 269-283, June.
    2. Adrian O’Hagan & Thomas Brendan Murphy & Luca Scrucca & Isobel Claire Gormley, 2019. "Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap," Computational Statistics, Springer, vol. 34(4), pages 1779-1813, December.

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