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Mean loglikelihood and higher-order approximations

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  • N. Reid
  • D. A. S. Fraser

Abstract

Higher-order approximations to p-values can be obtained from the loglikelihood function and a reparameterization that can be viewed as a canonical parameter in an exponential family approximation to the model. This approach clarifies the connection between Skovgaard (1996) and Fraser et al. (1999a), and shows that the Skovgaard approximation can be obtained directly using the mean loglikelihood function. Copyright 2010, Oxford University Press.

Suggested Citation

  • N. Reid & D. A. S. Fraser, 2010. "Mean loglikelihood and higher-order approximations," Biometrika, Biometrika Trust, vol. 97(1), pages 159-170.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:1:p:159-170
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    File URL: http://hdl.handle.net/10.1093/biomet/asq001
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    Cited by:

    1. Giuliana Cortese & Laura Ventura, 2013. "Accurate higher-order likelihood inference on $$P(Y>X)$$," Computational Statistics, Springer, vol. 28(3), pages 1035-1059, June.
    2. Erlis Ruli & Laura Ventura, 2021. "Can Bayesian, confidence distribution and frequentist inference agree?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 359-373, March.
    3. Donald Alan Pierce & Ruggero Bellio, 2017. "Modern Likelihood-Frequentist Inference," International Statistical Review, International Statistical Institute, vol. 85(3), pages 519-541, December.
    4. Ana-Maria Staicu, 2017. "Interview with Nancy Reid," International Statistical Review, International Statistical Institute, vol. 85(3), pages 381-403, December.

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