A Bayesian significance test of change for correlated observations

This paper presents a Bayesian significance test for a change in mean when observations are not independent. Using a noninformative prior, a unconditional test based on the highest posterior density credible set is determined. From a Gibbs sampler simulation study the effect of correlation on the performance of the Bayesian significance test derived under the assumption of no correlation is examined. This paper is a generalization of earlier studies by KIM (1991) to not independent observations

A Bayesian analysis of a change in the mean of independent normal sequence with contaminated observation

In this paper, we consider a Bayesian analysis of a change in the mean of independent gaussian samples in the presence of a single outlier. An unconditional Bayesian significance test for testing change versus no change is performed under consideration of non informative prior distribution of the parameters. From a numerical study using the Gibbs sampler algorithm, the effect of a contaminated observation on the performance of the Bayesian significance test of change is studied.Keywords: Gaussian models; Change-point; HPD region sets; p-value; Outliers.AMS 2010 Mathematics Subject Classification Objects: 91B84; 62F15; 62F0