Objective Bayes and conditional inference in exponential families
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.
Year of publication: |
2010
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Authors: | Diciccio, Thomas J. ; Young, G. Alastair |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 97.2010, 2, p. 497-504
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Publisher: |
Biometrika Trust |
Saved in:
Online Resource
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