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ArticleOriginal scientific text

Title

A Bayesian significance test of change for correlated observations

Authors 1, 2

Affiliations

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

Abstract

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.

Keywords

ENG
autoregressive model, change point, HPD region sets, p-value, Gibbs sampler

Bibliography

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Discussiones Mathematicae Probability and Statistics
2014/34/1-2
Export to PBN, Opens in new tab
Pages:
51-62
Main language of publication
English
Received
2014-05-12
Accepted
2014-07-23
Published
2014

DOI: 10.7151/dmps1169

Exact and natural sciences