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http://dx.doi.org/10.5351/KJAS.2014.27.4.589

Bayesian Detection of Multiple Change Points in a Piecewise Linear Function  

Kim, Joungyoun (Biostatistics and Clinical Epidemiology Center, Research Institute for Future Medicine, Samsung Medical Center)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.4, 2014 , pp. 589-603 More about this Journal
Abstract
When consecutive data follows different distributions(depending on the time interval) change-point detection infers where the changes occur first and then finds further inferences for each sub-interval. In this paper, we investigate the Bayesian detection of multiple change points. Utilizing the reversible jump MCMC, we can explore parameter spaces with unknown dimensions. In particular, we consider a model where the signal is a piecewise linear function. For the Bayesian inference, we propose a new Bayesian structure and build our own MCMC algorithm. Through the simulation study and the real data analysis, we verified the performance of our method.
Keywords
Change points; Bayesian inference; MCMC; reversible jump;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Barry, D. and Hartigan, J. A. (1993). A Bayesian analysis for change point problems, Journal of the American Statistical Association, 88, 309-319.
2 Beran, J. and Terrin, N. (1996). Testing for a change of the long-memory parameter, Biometrika, 83, 627-638.   DOI   ScienceOn
3 Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82, 711-732.   DOI   ScienceOn
4 Chernoff, H. and Zacks, S. (1964). Estimating the current mean of a normal distribution which is subjected to changes in time, The Annals of Mathematical Statistics, 35, 949-1417.   DOI
5 Chib, S. (1998). Estimation and comparison of multiple change-point models, Journal of Econometrics, 86, 211-241.
6 Fearnhead, P. (2006). Exact and efficient Bayesian inference for multiple changepoint problems, Statistics and Computing, 16, 203-213.   DOI
7 Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.   DOI   ScienceOn
8 Kim, J. W., Cho, S. and Yeo, I. K. (2009). A fast Bayesian detection of change points long-memory processes, The Korean journal of applied statistics, 22, 735-744.   과학기술학회마을   DOI
9 Stephens, D. A. (1994). Bayesian retrospective multiple-changepoint identification, Journal of the Royal Statistical Society. Series C (Applied Statistics), 43, 159-178.
10 Yao, Y. C. (1984). Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches, The Annals of Statistics, 12, 1151-1596.   DOI