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A refinement to approximate conditional inference

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  • Yang, Bo
  • Kolassa, John E.

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.

Suggested Citation

  • Yang, Bo & Kolassa, John E., 2005. "A refinement to approximate conditional inference," Statistics & Probability Letters, Elsevier, vol. 72(2), pages 103-112, April.
  • Handle: RePEc:eee:stapro:v:72:y:2005:i:2:p:103-112
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