A refinement to approximate conditional inference

Bo Yang and John E. Kolassa

Statistics & Probability Letters, 2005, vol. 72, issue 2, 103-112

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.

Keywords: Approximate; conditional; inference; Saddlepoint; approximation (search for similar items in EconPapers)
Date: 2005
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