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Computing highly accurate or exact P-values using importance sampling

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  • Lloyd, Chris J.

Abstract

Especially for discrete data, standard first order P-values can suffer from poor accuracy, even for quite large sample sizes. Moreover, different test statistics can give practically different results. There are several approaches to computing P-values which do not suffer these defects, such as parametric bootstrap P-values or the partially maximised P-values of Berger and Boos (1994).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1784-1794
    DOI: 10.1016/j.csda.2011.11.003
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    References listed on IDEAS

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    1. Lloyd, Chris J., 2010. "How close are alternative bootstrap P-values?," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1972-1976, December.
    2. Shi, Jianxin & Siegmund, David & Yakir, Benny, 2007. "Importance Sampling for Estimating p Values in Linkage Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 929-937, September.
    3. Lee, Stephen M.S. & Young, G. Alastair, 2005. "Parametric bootstrapping with nuisance parameters," Statistics & Probability Letters, Elsevier, vol. 71(2), pages 143-153, February.
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    Cited by:

    1. 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.

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