, Volume 23, Issue 2, pp 219–255 | Cite as

Extensions of some classical methods in change point analysis

  • Lajos Horváth
  • Gregory Rice
Invited Paper


A common goal in modeling and data mining is to determine, based on sample data, whether or not a change of some sort has occurred in a quantity of interest. The study of statistical problems of this nature is typically referred to as change point analysis. Though change point analysis originated nearly 70 years ago, it is still an active area of research and much effort has been put forth to develop new methodology and discover new applications to address modern statistical questions. In this paper we survey some classical results in change point analysis and recent extensions to time series, multivariate, panel and functional data. We also present real data examples which illustrate the utility of the surveyed results.


Change point analysis Sequential monitor Panel data Time series Functional data Linear models 

Mathematics Subject Classification

Primary 60F017 62M10 Secondary 60F05 60F25 62F05 60F12 62G30 62G10 62J05 62L20 62P12 62P20 



We are grateful to Marie Hušková, Stefan Fremdt and the participants of the Time Series Seminar at the University of Utah for pointing out mistakes in the earlier versions of this paper and to Daniela Jarušková and Brad Hatch for some of the data sets.


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© Sociedad de Estadística e Investigación Operativa 2014

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of UtahSalt Lake CityUSA

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