On matching confidence intervals and tests for some discrete distributions: methodological and computational aspects

Jan Klaschka () and Jenő Reiczigel ()
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Jan Klaschka: Institute of Computer Science of the Czech Academy of Sciences
Jenő Reiczigel: University of Veterinary Medicine Budapest

Computational Statistics, 2021, vol. 36, issue 3, No 11, 1775-1790

Abstract: Abstract Exact two-tailed tests and two-sided confidence intervals (CIs) for a binomial proportion or Poisson parameter by Sterne (Biometrika 41:117–129, 1954) or Blaker (Can J Stat 28(4):783–798, 2000) are successful in reducing conservatism of the Clopper–Pearson method. However, the methods suffer from an inconsistency between the tests and the corresponding CIs: In some cases, a parameter value is rejected by the test, though it lies in the CI. The problem results from non-unimodality of the test p value functions. We propose a slight modification of the tests that avoids the inconsistency, while preserving nestedness and exactness. Fast and accurate algorithms for both the test modification and calculation of confidence bounds are presented together with their theoretical background.

Keywords: Exact test; Sterne’s and Blaker’s exact confidence interval; Binomial model; Poisson model; Computational algorithm (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-00986-0

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