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Biometrika 1995 82(1):25-36; doi:10.1093/biomet/82.1.25
© 1995 by Biometrika Trust
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A Bayesian approach to approximate conditional inference

TREVOR J. SWEETING

Department of Mathematical and Computing Sciences, University of Surrey Guildford GU2 5XH, U. K.

Sweeting (1995) studies regular Bayesian and frequentist approximations within a unified framework in the case of a single parameter, and shows that higher-order approximations to sampling distributions arise from their Bayesian counterparts via an unsmoothing argument. In the present paper we extend this programme to include formulae in approximate conditional inference. In particular it is shown how Bayesian arguments may be used to derive some formulae developed by Barndorff-Nielsen (1980, 1983, 1986). The development proceeds in terms of likelihood roots.

Key Words: Approximate Bayesian inference • Approximate conditional inference • Kullback-Leibler distance • Likelihood root • Local ancillarity • Unsmoothing


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