A refinement to approximate conditional inference

  • Yang B
  • Kolassa J
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Abstract

This manuscript considers inference on a single parameter in a multivariate canonical exponential family, where the effect of nuisance parameters on the p-value is mitigated by conditioning on the event that the sufficient statistics associated with the nuisance parameters lie in a neighborhood about the observed value. This manuscript has three aims. First, we provide a method for approximating p-values using approximate conditioning that is more accurate than that presented by Pierce and Peters (Biometrika 86(1999) 265-277), at the price of greater computational difficulty. Second, we examine the sensitivity of approximate conditioning methods to the values of the nuisance parameters. Third, we describe a method for presenting a valid approximate-conditioning observed significance level accounting for this dependence on the nuisance parameters. © 2005 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Approximate conditional inference
  • Saddlepoint approximation

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Authors

  • Bo Yang

  • John E. Kolassa

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