Generalization of Jeffreys divergence-based priors for Bayesian hypothesis testing

Generalization of Jeffreys’ Divergence Based Priors for Bayesian Hypothesis testing

In this paper we introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence based (DB) priors. DB priors have simple forms and desirable properties, like information (finite sample) consistency; often, they are similar to other existing proposals like the intrinsic priors; moreov...

Bayesian Fuzzy Hypothesis Testing with Imprecise Prior Distribution

This paper considers the testing of fuzzy hypotheses on the basis of a Bayesian approach. For this, using a notion of prior distribution with interval or fuzzy-valued parameters, we extend a concept of posterior probability of a fuzzy hypothesis. Some of its properties are also put into investigation. The feasibility and effectiveness of the proposed methods are also cla...

Bayesian Hypothesis Testing 1 Running head: BAYESIAN HYPOTHESIS TESTING The Need for Bayesian Hypothesis Testing in Psychological Science

Bayesian hypothesis testing for proportions

Most clinical trials contain tests on proportions, usually they are answered by means of the Frequentist approach, nevertheless another valid option could be to solve them using a Bayesian approach. The Bayesian approach has the advantage that it is not restricted to only one alternative hypothesis. Moreover, the hypotheses to be tested do not necessarily overlap. In this paper we show a SAS® m...

HYPOTHESIS TESTING FOR AN EXCHANGEABLE NORMAL DISTRIBUTION

Consider an exchangeable normal vector with parameters ????2, and ?. On the basis of a vector observation some tests about these parameters are found and their properties are discussed. A simulation study for these tests and a few nonparametric tests are presented. Some advantages and disadvantages of these tests are discussed and a few applications are given.