This article argues that researchers do not need to completely abandon the p-value, the best-
known significance index, but should instead stop using significance levels that do not …

The full Bayesian significance test (FBST) for precise hypotheses is a Bayesian alternative to
the traditional significance tests based on p-values. The FBST is characterized by the e …

As one of the most commonly used data types, methods in testing or designing a trial for
binary endpoints from two independent populations are still being developed until recently …

The Non-Informative Nuisance Parameter Principle concerns the problem of how inferences
about a parameter of interest should be made in the presence of nuisance parameters. The …

Ensuring that the proposed probabilistic model accurately represents the problem is a
critical step in statistical modeling, as choosing a poorly fitting model can have significant …

The main goal of this work is to propose two new Bayesian approaches to equivalence tests
for two binomial proportions. As a result, we prove that these Bayesian hypothesis tests are …

The existing sequential test using Bernoulli distribution can only be applied when no
uncertainty/indeterminacy is found in testing the hypothesis. This paper introduces …

In this article, the local sensitivity of non linear prior quantities in Bayesian significance tests
with respect to the choice of a prior distribution is considered. We propose sensitivity indices …

Changepoint analysis (also known as segmentation analysis) aims to analyze an ordered,
one-dimensional vector in order to find locations where some characteristic of the data …

Changepoint analysis (also known as segmentation analysis) aims at analyzing an ordered,
one-dimensional vector, in order to find locations where some characteristic of the data …