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An efficient shrinkage bootstrap bias estimator for smooth functions of sample means

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Summary

Since it is not always possible to calculate bootstrap estimators, they are usually approximated by simulation. In this article, we propose a bootstrap bias estimator for smooth functions of sample means that has less mean squared error, due to the simulation process, than the ordinary bootstrap. The estimator is based on shrinking the bootstrap mean towards the original sample mean. It can easily be implemented while demanding almost no additional computational effort.

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Acknowledgments

The authors would like to thank the Associate Editor and two anonymous referees for their careful reading of the manuscript and for their helpful comments.

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Authors and Affiliations

  1. Departamento de Estadística e Investigatión Operativa, Universidad de Sevilla, 41012, Sevilla, Spain

    A. Muñoz-Reyes, J. L. Moreno-Rebollo, M. D. Jiménez-Gamero & J. Munoz-Garcia

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  1. A. Muñoz-Reyes
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  2. J. L. Moreno-Rebollo
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  3. M. D. Jiménez-Gamero
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  4. J. Munoz-Garcia
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Muñoz-Reyes, A., Moreno-Rebollo, J.L., Jiménez-Gamero, M.D. et al. An efficient shrinkage bootstrap bias estimator for smooth functions of sample means. Computational Statistics 20, 105–118 (2005). https://doi.org/10.1007/BF02736125

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  • DOI: https://doi.org/10.1007/BF02736125

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