Abstract
Under a robust Bayesian framework, we study p -values as post-data weights of evidence in hypothesis testing problems. For one-sided and two-sided hypothesis testing problems for the normal mean, p -values are considered as maximum likelihood estimates for some functions of the mean. We show that in contrast to Bayes estimates for reasonable families of priors, p -values are extreme for one-sided hypothesis testing problems and are moderate for two-sided problems. Implications on the controversies of ir/reconcilability of p -values in Casella and Berger [1987, J. Am. Statist. Assoc. 82, 106–111], Berger and Sellke [1987, J. Am. Statist. Assoc. 82, 112–122] and Berger and Delampady [1987, Statist. Sci. 2, 317–352] will also be addressed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
