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A numerical investigation of the accuracy of parametric bootstrap for discrete data

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  • Lloyd, Chris J.

Abstract

Standard first order tests have size error that decreases as m−1/2 where m is a measure of sample size. Parametric bootstrap tests use an exact calculation of the P-value, assuming nuisance parameters equal their null maximum likelihood estimates. It is commonly believed that their performance is driven by asymptotics, notwithstanding some confusion in the literature on asymptotic error rates.

Suggested Citation

  • Lloyd, Chris J., 2013. "A numerical investigation of the accuracy of parametric bootstrap for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 1-6.
    • Handle: RePEc:eee:csdana:v:61:y:2013:i:c:p:1-6
    DOI: 10.1016/j.csda.2012.09.015
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/555 is not listed on IDEAS
    2. Thomas J. Diciccio & G. Alastair Young, 2008. "Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families," Biometrika, Biometrika Trust, vol. 95(3), pages 747-758.
    3. Lloyd, Chris J., 2012. "Computing highly accurate or exact P-values using importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1784-1794.
    4. D.A.S. Fraser & Judith Rousseau, 2008. "Studentization and deriving accurate p-values," Biometrika, Biometrika Trust, vol. 95(1), pages 1-16.
    5. Lee, Stephen M.S. & Young, G. Alastair, 2005. "Parametric bootstrapping with nuisance parameters," Statistics & Probability Letters, Elsevier, vol. 71(2), pages 143-153, February.
    Full references (including those not matched with items on IDEAS)

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