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Objective Bayes and conditional inference in exponential families

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  • Thomas J. Diciccio
  • G. Alastair Young
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    Abstract

    Objective Bayes methodology is considered for conditional frequentist inference about a canonical parameter in a multi-parameter exponential family. A condition is derived under which posterior Bayes quantiles match the conditional frequentist coverage to a higher-order approximation in terms of the sample size. This condition is on the model, not on the prior, and it ensures that any first-order probability matching prior in the unconditional sense automatically yields higher-order conditional probability matching. Objective Bayes methods are compared to parametric bootstrap and analytic methods for higher-order conditional frequentist inference. Copyright 2010, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/biomet/asq002
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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 97 (2010)
    Issue (Month): 2 ()
    Pages: 497-504

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    Handle: RePEc:oup:biomet:v:97:y:2010:i:2:p:497-504

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