P-value calibration in multiple hypotheses testing Articles uri icon

publication date

  • August 2017

start page

  • 2875

end page

  • 2886

issue

  • 18

volume

  • 36

international standard serial number (ISSN)

  • 0277-6715

electronic international standard serial number (EISSN)

  • 1097-0258

abstract

  • As p-values are the most common measures of evidence against a hypothesis, their calibration with respect to null hypothesis conditional probability is important in order to match frequentist unconditional inference with the Bayesian ones. The Selke, Bayarri and Berger calibration is one of the most popular attempts to obtain such a calibration. This relies on the theoretical sampling null distribution of p-values, which is the well-known Uniform(0,1), but arising only.

keywords

  • bayes factor lower bound; non-parametric bayes; objective bayes; significance testing