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Multiple change-point detection of multivariate mean vectors with the Bayesian approach

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  • Cheon, Sooyoung
  • Kim, Jaehee

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.

Suggested Citation

  • Cheon, Sooyoung & Kim, Jaehee, 2010. "Multiple change-point detection of multivariate mean vectors with the Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 406-415, February.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:406-415
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    References listed on IDEAS

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    1. De Gooijer, Jan G., 2006. "Detecting change-points in multidimensional stochastic processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1892-1903, December.
    2. Loschi, R.H. & Cruz, F.R.B., 2005. "Extension to the product partition model: computing the probability of a change," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 255-268, February.
    3. Liang, Faming & Liu, Chuanhai & Carroll, Raymond J., 2007. "Stochastic Approximation in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 305-320, March.
    4. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    5. Venter, J. H. & Steel, S. J., 1996. "Finding multiple abrupt change points," Computational Statistics & Data Analysis, Elsevier, vol. 22(5), pages 481-504, September.
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    1. Jaehee Kim & Chulwoo Jeong, 2016. "A Bayesian multiple structural change regression model with autocorrelated errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1690-1705, July.
    2. Noriah Al-Kandari & Emad-Eldin Aly, 2014. "An ANOVA-type test for multiple change points," Statistical Papers, Springer, vol. 55(4), pages 1159-1178, November.
    3. Chen, Cathy W.S. & Chan, Jennifer S.K. & So, Mike K.P. & Lee, Kevin K.M., 2011. "Classification in segmented regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2276-2287, July.
    4. David Hallac & Peter Nystrup & Stephen Boyd, 2019. "Greedy Gaussian segmentation of multivariate time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 727-751, September.

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