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Bayes factors based on test statistics
Author(s) -
Johnson Valen E.
Publication year - 2005
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2005.00521.x
Subject(s) - bayes factor , bayes' theorem , statistics , statistical hypothesis testing , mathematics , bayes error rate , bayesian statistics , null hypothesis , bayes' rule , econometrics , computer science , bayes classifier , bayesian probability , bayesian inference
Summary. Traditionally, the use of Bayes factors has required the specification of proper prior distributions on model parameters that are implicit to both null and alternative hypotheses. I describe an approach to defining Bayes factors based on modelling test statistics. Because the distributions of test statistics do not depend on unknown model parameters, this approach eliminates much of the subjectivity that is normally associated with the definition of Bayes factors. For standard test statistics, including the χ 2 ‐, F ‐, t ‐ and z ‐statistics, the values of Bayes factors that result from this approach have simple, closed form expressions.