English, Article edition: BAYESIAN IDENTIFICATION OF MULTIPLE CHANGE POINTS IN POISSON DATA R. H. LOSCHI; F. R. B. CRUZ

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Physical Description
  • article
Language
  • English

Edition details

Title
  • BAYESIAN IDENTIFICATION OF MULTIPLE CHANGE POINTS IN POISSON DATA
Author
  • R. H. LOSCHI
  • F. R. B. CRUZ
Physical Description
  • article
Notes
  • The identification of multiple change points is a problem shared by many subject areas, including disease and criminality mapping, medical diagnosis, industrial control, and finance. An algorithm based on the Product Partition Model (PPM) is developed to solve the multiple change point identification problem in Poisson data sequences. In order to address the PPM, a simple and easy way to implement Gibbs sampling scheme is derived. A sensitivity analysis is performed, for different prior specifications. The algorithm is then applied to the analysis of a real data sequence. The results show that the method is quite effective and provides useful inferences.
  • Beta prior distribution, student-t distribution, Yao's cohesions, Gibbs sampling
  • RePEc:wsi:acsxxx:v:08:y:2005:i:04:p:465-482
Language
  • English
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