A novel approach is proposed for the problem of testing statistical hypotheses about the fuzzy mean of a fuzzy random variable.The concept of the (uniformly) most powerful test is extended to the (uniformly) most powerful fuzzy-valued test in which the test function is a fuzzy set representing the degrees of rejection and acceptance of the hypothesis of interest.For this purpose, the concepts o...

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternat...

A conditioning on the event of having selected one model from a set possibly misspecified normal linear regression models, leads to construction uniformly optimal conditional confidence distributions. They can be used for valid post-selection inference. The constructed distributions are finite sample exact and encompass all information regarding focus parameter in model. This includes intervals...

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternat...

The theory behind Uniformly Most Powerful (UMP) composite binary hypothesis testing is mature and well defined in centralized detection where all observations are directly accessible at one central node. However, within the area of decentralized detection, UMP tests have not been researched, even though tests of this nature have properties that are highly desirable. The purpose of this research...

Let Θ = Θ0 ∪Θ1 be a parameter space. Consider a parametric family {f(x|θ), θ ∈ Θ}. Suppose we want to test the null hypothesis, H0, that θ ∈ Θ0 against the alternative, Ha, that θ ∈ Θ1. Let C be some critical set. Then the probability that the null hypothesis is rejected is given by β(θ) = Pθ{X ∈/ C}. Recall that the test based on C has level α if α ≥ supθ Θ0 β(θ). The restriction of β(·) on Θ1...