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Multiple change-point detection of multivariate mean vectors with the Bayesian approach
- Authors
- Cheon, Sooyoung; Kim, Jaehee
- Issue Date
- 1-2월-2010
- Publisher
- ELSEVIER SCIENCE BV
- Citation
- COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.54, no.2, pp.406 - 415
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMPUTATIONAL STATISTICS & DATA ANALYSIS
- Volume
- 54
- Number
- 2
- Start Page
- 406
- End Page
- 415
- ISSN
- 0167-9473
- Abstract
- Bayesian multiple change-point models are proposed for multivariate means. The models require that the data be from a multivariate normal distribution with a truncated Poisson prior for the number of change-points and conjugate priors for the distributional parameters. We apply the stochastic approximation Monte Carlo (SAMC) algorithm to the multiple change-point detection problems. Numerical results show that SAMC makes a significant improvement over RJMCMC for complex Bayesian model selection problems in change-point estimation. (C) 2009 Elsevier B.V. All rights reserved.
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