539
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

Rank-based multiple change-point detection

ORCID Icon, &
Pages 3438-3454 | Received 10 Sep 2018, Accepted 27 Feb 2019, Published online: 03 Apr 2019

References

  • Aue, A., and L. Horváth. 2013. Structural breaks in time series. Journal of Time Series Analysis 34 (1):1–16. doi:10.1111/j.1467-9892.2012.00819.x.
  • Aue, A., R. C. Y. Cheung, T. C. M. Lee, and M. Zhong. 2014. Segemented model selection in quantile regression using the minimum description length principle. Journal of the American Statistical Association 109 (507):1241–56. doi:10.1080/01621459.2014.889022.
  • Bai, J., and P. Perron. 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66 (1):47–78. doi:10.2307/2998540.
  • Bai, J. 1998. Estimation of multiple-regime regressions with least absolutes deviation. Journal of Statistical Planning and Inference 74 (1):103–34. doi:10.1016/S0378-3758(98)00082-2.
  • Boysen, L., A. Kempe, V. Liebscher, A. Munk, and O. Wittich. 2009. Consistencies and rates of convergence of jump-penalized least squares estimators. The Annals of Statistics 35:157–83. doi:10.1214/07-AOS558.
  • Brodsky, B. E., and B. S. Darkhovsky. 1993. Nonparametric methods in changepoint problems. Berlin, Germany: Springer Science & Business Media.
  • Carlstein, E. 1988. Nonparametric change-point estimation. The Annals of Statistics 16 (1):188–97. doi:10.1214/aos/1176350699.
  • Celisse, A., G. Marot, and M. Pierre-Jean. 2018. New efficient algorithms for multiple change-point detection with kernels. Computational Statistics & Data Analysis 128:200–20. doi:10.1016/j.csda.2018.07.002.
  • Chen, J., and A. K. Gupta. 2011. Parametric statistical change point analysis: With applications to genetics, medicine, and finance. Berlin, Germany: Springer Science & Business Media.
  • Csörgö, M., and L. Horváth. 1997. Limit theorems in Change-Point analysis. New York: Wiley.
  • Darkhovshk, B. S. 1976. A non-parametric method for the a posteriori detection of the “disorder” time of a sequence of independent random variables. Theory of Probability and Its Applications 21:178–83.
  • Dümbgen, L. 1991. The asymptotic behaviour of some nonparametric changepoints estimators. Annals of Statistics 19:1471–95.
  • Eichinger, B., and C. Kirch. 2018. A MOSUM procedure for the estimation of multiple random change points. Bernoulli 24 (1):526–64. doi:10.3150/16-BEJ887.
  • Fasola, S., V. M. Muggeo, and H. Küchenhoff. 2018. A heuristic, iterative algorithm for change-point detection in abrupt change models. Computational Statistics 33 (2):997–1015. doi:10.1007/s00180-017-0740-4.
  • Fearnhead, P., and G. Rigaill. 2018. Changepoint detection in the presence of outliers. Journal of the American Statistical Association :1–15. doi:10.1080/01621459.2017.1385466.
  • Frick, K., A. Munk, and H. Sieling. 2014. Multiscale change point inference. Journal of the Royal Statistical Society Series B-Statistical Methodology 76 (3):495–580. doi:10.1111/rssb.12047.
  • Fryzlewicz, P. 2014. Wild binary segmentation for multiple change-point detection. The Annals of Statistics 42 (6):2243–81. doi:10.1214/14-AOS1245.
  • Garreau, D., and S. Arlot. 2018. Consistent change-point detection with kernels. Electronic Journal of Statistics 12 (2):4440–86. doi:10.1214/18-EJS1513.
  • Gerstenberger, C. 2018. Robust wilcoxon-type estimation of change-point location under short-range dependence. Journal of Time Series Analysis 39 (1):90–104. doi:10.1111/jtsa.12268.
  • Gombay, E., and M. Hušková. 1998. Rank based estimators of the change-point. Journal of Statistical Planning and Inference 67 (1):137–54. doi:10.1016/S0378-3758(97)00099-2.
  • Han, F. 2018. An exponential inequality for u-statistics under mixing conditions. Journal of Theoretical Probability 31 (1):556–78. doi:10.1007/s10959-016-0722-4.
  • Hao, N., Y. Niu, and H. Zhang. 2013. Multiple change-point detection via a screening and ranking algorithm. Statistica Sinica 23:1553–72.
  • Harle, F., F. Chatelain, and C. Gouy-Pailler. 2014. Rank-based multiple change-point detection in multivariate time series. 22nd European Signal Processing Conference (EUSIPCO), 1337–41.
  • Haynes, K., P. Fearnhead, and I. A. Eckley. 2017. A computationally efficient nonparametric approach for changepoint detection. Statistics and Computing 27 (5):1293–305. doi:10.1007/s11222-016-9687-5.
  • Hoeffding, W. 1963. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58 (301):13. doi:10.2307/2282952.
  • Hubert, L., and P. Arabie. 1985. Comparing partitions. Journal of Classification 2 (1):193–218. doi:10.1007/BF01908075.
  • Hušková, M. 1997. Limit theorems for rank statistics. Statists & Probability Letters 31:45–55. doi:10.1016/S0167-7152(96)00055-7.
  • Jandhyala, V., S. Fotopoulos, I. MacNeill, and P. Liu. 2013. Inference for single and multiple change-points in time series. Journal of Time Series Analysis 34 (4):423–46. doi:10.1111/jtsa.12035.
  • Jeng, X. J., T. T. Cai, and H. Li. 2010. Optimal sparse segment identification with application in copy number variation analysis. Journal of the American Statistical Association 105 (491):1156–66. doi:10.1198/jasa.2010.tm10083.
  • Killick, R., P. Fearnhead, and I. A. Eckley. 2012. Optimal detection of change-points with a linear computational cost. Journal of the American Statistical Association 107 (500):1590–8. doi:10.1080/01621459.2012.737745.
  • Lavielle, M. 2005. Using penalized contrasts for the change-point problem. Signal Processing 85 (8):1501–10. doi:10.1016/j.sigpro.2005.01.012.
  • Lee, C. B. 1996. Nonparametric multiple change-point estimators. Statistics & Probability Letters 27:195–304.
  • Lung-Yut-Fong, A., C. Lévy-Leduc, and O. Cappé. 2015. Homogeneity and change-point detection tests for multivariate data using rank statistics. Journal de la Société Franaise de Statistique 156:133–62.
  • 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. doi:10.1080/01621459.2013.849605.
  • Muggeo, V. M., and G. Adelfio. 2011. Efficient change point detection for genomic sequences of continuous measurements. Bioinformatics 27 (2):161–6. doi:10.1093/bioinformatics/btq647.
  • Oka, T., and Z. Qu. 2011. Estimating structural changes in regression quantiles. Journal of Econometrics 162 (2):248–67. doi:10.1016/j.jeconom.2011.01.005.
  • Page, E. S. 1954. Continuous inspection schemes. Biometrika 41 (1–2):100–15.
  • Pein, F., H. Sieling, and A. Munk. 2017. Heterogeneous change point inference. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79 (4):1207–27. doi:10.1111/rssb.12202.
  • Pierre-Jean, M., G. Rigaill, and P. Neuvial. 2015. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics 16 (4):600–15. doi:10.1093/bib/bbu026.
  • Qu, Z. 2008. Testing for structural change in regression quantiles. Journal of Econometrics 146 (1):170–84. doi:10.1016/j.jeconom.2008.08.006.
  • Rigaill, G. 2015. A pruned dynamic programming algorithm to recover the best segmentations with 1 to Kmax change-points. Journal de la Société Francaise de Statistique 156:180–205.
  • Yao, Y. C. 1988. Estimating the numbber of change-points via schwarz’ criterion. Statistics & Probability Letters 6:181–9. doi:10.1016/0167-7152(88)90118-6.
  • Zhang, N. R., and D. O. Siegmund. 2007. A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics 63 (1):22–32. doi:10.1111/j.1541-0420.2006.00662.x.
  • Zou, C., Y. Liu, P. Qin, and Z. Wang. 2007. Empirical likelihood ratio test for the change-point problem. Statistics & Probability Letters 77:374–82. doi:10.1016/j.spl.2006.08.003.
  • Zou, C., G. Yin, L. Feng, and Z. Wang. 2014. Nonparametric maximum likelihood approach to multiple change-point problems. The Annals of Statistics 42 (3):970–1002. doi:10.1214/14-AOS1210.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.