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Bayesian change-point problem using Bayes factor with hierarchical prior distribution

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  • Myoungjin Jung
  • Seongho Song
  • Younshik Chung

Abstract

We consider the hierarchical Bayesian models of change-point problem in a sequence of random variables having either normal population or skew-normal population. Further, we consider the problem of detecting an influential point concerning change point using Bayes factors. Our proposed models are illustrated with the real data example, the annual flow volume data of Nile River at Aswan from 1871 to 1970. The result using our proposed models indicated the largest influential observation in the year 1888 among outliers. We have shown that it is useful to measure the influence of observations on Bayes factors. Here, we consider omitting single observation as well.

Suggested Citation

  • Myoungjin Jung & Seongho Song & Younshik Chung, 2017. "Bayesian change-point problem using Bayes factor with hierarchical prior distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(3), pages 1352-1366, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:3:p:1352-1366
    DOI: 10.1080/03610926.2015.1019143
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