, Volume 23, Issue 2, pp 270–275 | Cite as

Comments on: Extensions of some classical methods in change point analysis

  • Claudia KirchEmail author

First, I would like to congratulate the authors on a very nice review on recent advances in change point analysis. Such a survey is certainly an invaluable contribution for the statistical community. Even though it is an impossible task to shed detailed light on all aspects of recent papers in that area without writing a book, I would like to complement the review in two points, namely change point procedures for count time series as well as resampling methods in change point analysis, which have only been mentioned in passing in the above article. Furthermore, I would like to draw the attention of the interested reader to a recent survey-like article of Hušková and Hlávka (2012), which gives a very detailed review of many new articles concerned with sequential testing as discussed in Section 5 of the above paper.

Change point tests for count time series

Many data sets of interest consist of values on the integers such as binary data (Has there been rainfall in a certain period of...


Change Point Partial Likelihood Change Point Analysis Empirical Characteristic Function Change Point Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The position of the author was financed by the Stifterverband für die deutsche Wissenschaft by funds of the Claussen-Simon-trust.


  1. Ciuperca G (2013) Two tests for sequential detection of a change-point in a nonlinear model. J Stat Plann Infer 143:1719–1743CrossRefzbMATHMathSciNetGoogle Scholar
  2. Doukhan P, Kegne W (2013) Inference and testing for structural change in time series of counts model. arXiv:1305.1751
  3. Fokianos K (2012) Count time series models. In: Rao CR, Subba Rao T (eds) Handbook in statistics. Time series-methods and applications, vol 30. Elsevier B.V, Amsterdam, pp 315–348CrossRefGoogle Scholar
  4. Fokianos K, Gombay E, Hussein A (2014) Retrospective change detection for binary time series models. J Stat Plann Infer 145:102–112Google Scholar
  5. Franke J, Kirch C, Tadjuidje Kamgaing J (2012) Changepoints in times series of counts. J Time Ser Anal 33:757–770CrossRefzbMATHMathSciNetGoogle Scholar
  6. Hlávka Z, Hušková M, Kirch C, Meintanis S (2012) Monitoring changes in the error distribution of autoregressive models based on fourier methods. Test 21:605–634CrossRefzbMATHMathSciNetGoogle Scholar
  7. Hlávka Z, Hušková M, Kirch C, Meintanis S (2014) Bootstrap procedures for on-line monitoring of changes in autoregressive models. Commun Stat Simulation ComputGoogle Scholar
  8. Hudecová S (2013) Structural changes in autoregressive models for binary time series. J Stat Plann Inf 143(10):1744–1752CrossRefzbMATHGoogle Scholar
  9. Hušková M (2004) Permutation principle and bootstrap in change point analysis. Fields Inst Commun 44:273–291Google Scholar
  10. Hušková M, Hlávka Z (2012) Nonparametric sequential monitoring. Seq Anal 31(3):278–296zbMATHGoogle Scholar
  11. Hušková M, Kirch C (2008) Bootstrapping confidence intervals for the change-point of time series. J Time Ser Anal 29:947–972CrossRefzbMATHMathSciNetGoogle Scholar
  12. Hušková M, Kirch C (2010) A note on studentized confidence intervals in change-point analysis. Comput Stat 25:269–289CrossRefzbMATHGoogle Scholar
  13. Hušková M, Kirch C (2012) Bootstrapping sequential change-point tests for linear regression. Metrika 75:673–708CrossRefzbMATHMathSciNetGoogle Scholar
  14. Hušková M, Meintanis S (2006) Change point analysis based on empirical characteristic functions. Metrika 63(2):145–168CrossRefzbMATHMathSciNetGoogle Scholar
  15. Kirch C (2007) Block permutation principles for the change analysis of dependent data. J Stat Plann Infer 137:2453–2474CrossRefzbMATHMathSciNetGoogle Scholar
  16. Kirch C (2008) Bootstrapping sequential change-point tests. Seq Anal 27:330–349CrossRefzbMATHMathSciNetGoogle Scholar
  17. Kirch C, Politis DN (2011) Tft-bootstrap: resampling time series in the frequency domain to obtain replicates in the time domain. Ann Stat 39:1427–1470CrossRefzbMATHMathSciNetGoogle Scholar
  18. Kirch C, Tadjuidje Kamgaing J (2014) Monitoring time series based on estimating functions. University of KaiserslauternGoogle Scholar
  19. Kirch C, Tajduidje Kamgaing J (2014) Detection of change points in discrete valued time series. In: Handbook of discrete valued time series. In: Davis RA, Holan SA, Lund RB, Ravishanker NGoogle Scholar
  20. Weiss CH (2011) Detecting mean increases in poisson inar(1) processes with ewma control charts. J Appl Stat 38:383–398CrossRefMathSciNetGoogle Scholar
  21. Weiß CH, Testik MC (2011) The poisson inar(1) cusum chart under overdispersion and estimation error. IIE Trans 43(11):805–818Google Scholar
  22. Yontay P, Weiß CH, Testik MC, Bayindir ZP (2012) A two-sided cumulative sum chart for first-order integer-valued autoregressive processes of poisson counts. Qual Reliab Eng Int 29:33–42CrossRefGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2014

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT)Institute of StochasticsKarlsruheGermany

Personalised recommendations