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Diagnostics of prior-data agreement in applied Bayesian analysis

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  • Nicolas Bousquet

Abstract

This article focused on the definition and the study of a binary Bayesian criterion which measures a statistical agreement between a subjective prior and data information. The setting of this work is concrete Bayesian studies. It is an alternative and a complementary tool to the method recently proposed by Evans and Moshonov, [M. Evans and H. Moshonov, Checking for Prior-data conflict, Bayesian Anal. 1 (2006), pp. 893-914]. Both methods try to help the work of the Bayesian analyst, from preliminary to the posterior computation. Our criterion is defined as a ratio of Kullback-Leibler divergences; two of its main features are to make easy the check of a hierarchical prior and be used as a default calibration tool to obtain flat but proper priors in applications. Discrete and continuous distributions exemplify the approach and an industrial case study in reliability, involving the Weibull distribution, is highlighted.

Suggested Citation

  • Nicolas Bousquet, 2008. "Diagnostics of prior-data agreement in applied Bayesian analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1011-1029.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:1011-1029
    DOI: 10.1080/02664760802192981
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    Citations

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    Cited by:

    1. Berthold-Georg Englert & Michael Evans & Gun Ho Jang & Hui Khoon Ng & David Nott & Yi-Lin Seah, 2021. "Checking for model failure and for prior-data conflict with the constrained multinomial model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(8), pages 1141-1168, November.
    2. Evelien Schat & Rens van de Schoot & Wouter M Kouw & Duco Veen & Adriënne M Mendrik, 2020. "The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. Rufo, M.J. & Pérez, C.J. & Martín, J., 2010. "Merging experts' opinions: A Bayesian hierarchical model with mixture of prior distributions," European Journal of Operational Research, Elsevier, vol. 207(1), pages 284-289, November.
    4. Rufo, M.J. & Martín, J. & Pérez, C.J., 2009. "Inference on exponential families with mixture of prior distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3271-3280, July.
    5. Remy, Emmanuel & Corset, Franck & Despréaux, Stéphane & Doyen, Laurent & Gaudoin, Olivier, 2013. "An example of integrated approach to technical and economic optimization of maintenance," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 8-19.

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