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2014 | 34 | 1-2 | 51-62
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A Bayesian significance test of change for correlated observations

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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.
  • Departemnet of Mathematics and Computer Science, University of Adrar, National Road No. 06, Adrar, Algeria
  • Departement of Probability and Statistics, USTHB, P.O. Box 32 EL Alia 16111 Bab Ezzouar, Algiers, Algeria
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