APPLYING THE PRODUCT PARTITION MODEL TO THE IDENTIFICATION OF MULTIPLE CHANGE POINTS

R. H. Loschi () and F. R. B. Cruz ()
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R. H. Loschi: Departamento de Estatística, Universidade Federal de Minas Gerais, 31270-901 – Belo Horizonte – MG, Brazil
F. R. B. Cruz: Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst MA 01003, USA

Advances in Complex Systems (ACS), 2002, vol. 05, issue 04, 371-387

Abstract: The multiple change point identification problem may be encountered in many subject areas, including disease mapping, medical diagnosis, industrial control, and finance. One appealing way of tackling the problem is through the product partition model (PPM), a Bayesian approach. Nowadays, practical applications of Bayesian methods have attracted attention perhaps because of the generalized use of powerful and inexpensive personal computers. A Gibbs sampling scheme, simple and easy to implement, is used to obtain the estimates. We apply the algorithm to the analysis of two important stock market data in Brazil. The results show that the method is efficient and effective in analyzing change point problems.

Keywords: Beta prior distribution; relevance; Student-tdistribution; Yao's cohesions (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1142/S0219525902000651

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