From p-Values to Posterior Probabilities of Null Hypotheses

Autor:	Daiver Vélez Ramos, Luis R. Pericchi Guerra, María Eglée Pérez Hernández
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	p-value calibration Bayes factor linear model pseudo-p-value adaptive levels Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 25, Iss 4, p 618 (2023)
Druh dokumentu:	article
ISSN:	25040618 1099-4300
DOI:	10.3390/e25040618
Popis:	Minimum Bayes factors are commonly used to transform two-sided p-values to lower bounds on the posterior probability of the null hypothesis, in particular the bound −e·p·log(p). This bound is easy to compute and explain; however, it does not behave as a Bayes factor. For example, it does not change with the sample size. This is a very serious defect, particularly for moderate to large sample sizes, which is precisely the situation in which p-values are the most problematic. In this article, we propose adjusting this minimum Bayes factor with the information to approximate an exact Bayes factor, not only when p is a p-value but also when p is a pseudo-p-value. Additionally, we develop a version of the adjustment for linear models using the recent refinement of the Prior-Based BIC.
Databáze:	Directory of Open Access Journals
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