Extensions of some classical methods in change point analysis

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.