Nonparametric product partition models for multiple change-points analysis

References

• Barry, D., and J. A. Hartigan. 1992. Product partition models for change point problems. The Annals of Statistics 20 (1):260–79.
   Google Scholar
• Barry, D., and J. A. Hartigan. 1993. A Bayesian analysis for change point problems. Journal of the American Statistical Association 88 (421):309–19.
   Google Scholar
• Binder, D. A. 1978. Bayesian cluster analysis. Biometrika 65 (1):31–38.
   Google Scholar
• Binder, D. A. 1981. Approximations to Bayesian clustering rules. Biometrika 68 (1):275–85.
   Google Scholar
• Blasi, P. D., S. Favaro, A. Lijoi, R. H. Mena, I. Prünster, and M. Ruggiero. 2015. Are Gibbs-type priors the most natural generalization of the dirichlet process? IEEE Transactions on Pattern Analysis and Machine Intelligence 37:212–29.
   Google Scholar
• Bormetti, G., M. E. De Giuli, D. Delpini, and C. Tarantola. 2012. Bayesian value-at-risk with product partition models. Quantitative Finance 12 (5):769–80.
   Google Scholar
• Brodsky, B., and B. Darkhovsky. 2000. Non-Parametric statistical diagnosis Problems and methods. The Netherlands: Kluwer Academic Publishers.
   Google Scholar
• Chen, J., and A. K. Gupta. 2012. Parametric Statistical Change Point Analysis: With Applications to Genetics, Birkhäuser Boston, Medicine, and Finance, Boston.
   Google Scholar
• Csörgö, M. and L. Horváth. 1997. Limit theorems in change-point analysis. Wiley Series in Probability and Statistics. Chichester, UK: John Wiley and Sons.
   Google Scholar
• Eckley, I., P. Fearnhead, and R. Killick. 2011. Analysis of changepoint models. In D. Barber, T. Cemgil & S. Chiappa, eds, ‘Bayesian Time Series Models’, Cambridge: Cambridge University Press, pp. 205–224.
   Google Scholar
• Erdman, C., and J. W. Emerson. 2007. bcp: An R package for performing a Bayesian analysis of change point problems. Journal of Statistical Software 23 (3):1–13.
   Google Scholar
• Fuentes-García, R., R. H. Mena, and S. G. Walker. 2010. A probability for classification based on the dirichlet process mixture model. Journal of Classification 27 (3):389–403.
   Google Scholar
• Hahn, P. R., and C. M. Carvalho. 2015. Decoupling shrinkage and selection in Bayesian linear models: A posterior summary perspective. Journal of the American Statistical Association 110 (509):230–35.
   Google Scholar
• Hartigan, J. A. 1990. Partition models. Communications in Statistics (Theory and Methods) 19 (8):2745–56.
   Google Scholar
• Hsu, D. A. 1979. Detecting shifts of parameter in Gamma sequences with applications to stock price and air traffic flow analysis. Journal of the American Statistical Association 74 (365):31–40.
   Google Scholar
• Hupe, P. 2011. GLAD: Gain and loss analysis of DNA. R package version 2.20.0.
   Google Scholar
• Hurn, M., A. Justel, and C. P. Robert. 2003. Estimating mixtures of regressions. Journal of Computational and Graphical Statistics 12 (1):55–79.
   Google Scholar
• Kehagias, A., A. Nicolaou, V. Petridis, and P. Fragkou. 2004. Text segmentation by product partition models and dynamic programming. Mathematical and Computer Modeling 39:209–17.
   Google Scholar
• Killick, R., P. Fearnhead, and I. A. Eckley. 2012. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 107 (500):1590–98.
   Google Scholar
• Lau, J. W., and P. J. Green. 2007. Bayesian model based clustering procedures. Journal of Computational and Graphical Statistics 16:526–58.
   Google Scholar
• Loschi, R. H. 2002. An analysis of the influence of some prior specifications in the identification of change points via product partition model. Computational Statistics and Data Analysis 39 (4):477–501.
   Google Scholar
• Loschi, R. H., and F. R. B. Cruz. 2005. Extension to the product partition model: Computing the probability of a change. Computational Statistics and Data Analysis 48 (2):255–68.
   Google Scholar
• Loschi, R. H., F. R. B. Cruz, P. L. Iglesias, and R. Arellano-Valle. 2003. A Gibbs sampling scheme to the product partition model: An application to change-point problems. Computers and Operations Research 30 (3):463–82.
   Google Scholar
• Loschi, R. H., F. R. B. Cruz, R. H. C. Takahashi, P. L. Iglesias, R. B. Arellano-Valle, and J. MacGregor Smith. 2008. A note on Bayesian identification of change points in data sequences. Computers and Operations Research 35 (1):156–70.
   Google Scholar
• Loschi, R. H., J. G. Pontel, and F. R. B. Cruz. 2010. Multiple change-point analysis for linear regression models. Chilean Journal of Statistics 1 (2):93–112.
   Google Scholar
• Martínez, A. F., and R. H. Mena. 2014. On a nonparametric change point detection model in markovian regimes. Bayesian Analysis 9 (4):823–58.
   Google Scholar
• Matteson, D. S., and N. A. James. 2014. A nonparametric approach for multiple change point analysis of multivariate data. Journal of the American Statistical Association 109 (505):334–45.
   Google Scholar
• Mira, A., and S. Petrone. 1996. Bayesian hierarchical non-parametric inference for change-point problems. In Bayesian Statistics 5, ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, 693–703. USA: Oxford University Press.
   Google Scholar
• Monteiro, J. V. D., R. M. Assunção, and R. H. Loschi. 2011. Product partition models with correlated parameters. Bayesian Analysis 6 (4):691–726.
   Google Scholar
• Quintana, F. A., and P. L. Iglesias. 2003. Bayesian clustering and product partition models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65 (2):557–74.
   Google Scholar
• Quintana, F. A., P. L. Iglesias, and H. Bolfarine. 2005. Bayesian Identification of outliers and change-points in measurement error models. Advances in Complex Systems 8 (04):433–49.
   Google Scholar
• Core, Team R. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Rand, W. M. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66 (336):846–50.
   Google Scholar
• Snijders, A. M., N. Nowak, R. Segraves, S. Blackwood, N. Brown, J. Conroy, G. Hamilton, A. K. Hindle, B. Huey, K. Kimura, S. Law, K. Myambo, J. Palmer, B. Ylstra, J. P. Yue, J. W. Gray, A. N. Jain, D. Pinkel, and D. G. Albertson. 2001. Assembly of microarrays for genome-wide measurement of DNA copy number. Nature Genetics 29 (3):263–64.
   Google Scholar
• Troyanskaya, O., M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman. 2001. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (6):520–25.
   Google Scholar
• Yao, Y.-C. 1984. Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches. The Annals of Statistics 12 (4):1434–47.
   Google Scholar
• Yau, C., and C. C. Holmes. 2013. A decision-theoretic approach for segmental classification. The Annals of Applied Statistics 7 (3):1814–35.
   Google Scholar
• Yau, C., O. Papaspiliopoulos, G. O. Roberts, and C. C. Holmes. 2011. Bayesian non-parametric hidden Markov models with applications in genomics. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 (1):37–57.
   Google Scholar
• Zeileis, A., F. Leisch, K. Hornik, and C. Kleiber. 2002. Strucchange: An R package for testing for structural change in linear regression models. Journal of Statistical Software 7 (2):1–38.
   Google Scholar