Computational Statistics

, Volume 33, Issue 2, pp 997–1015 | Cite as

A heuristic, iterative algorithm for change-point detection in abrupt change models

  • Salvatore Fasola
  • Vito M. R. Muggeo
  • Helmut Küchenhoff
Original Paper
  • 129 Downloads

Abstract

Change-point detection in abrupt change models is a very challenging research topic in many fields of both methodological and applied Statistics. Due to strong irregularities, discontinuity and non-smootheness, likelihood based procedures are awkward; for instance, usual optimization methods do not work, and grid search algorithms represent the most used approach for estimation. In this paper a heuristic, iterative algorithm for approximate maximum likelihood estimation is introduced for change-point detection in piecewise constant regression models. The algorithm is based on iterative fitting of simple linear models, and appears to extend easily to more general frameworks, such as models including continuous covariates with possible ties, distinct change-points referring to different covariates, and further covariates without change-point. In these scenarios grid search algorithms do not straightforwardly apply. The proposed algorithm is validated through some simulation studies and applied to two real datasets.

Keywords

Piecewise constant model Approximate maximum likelihood Model linearization Grid search limitations 

Notes

Acknowledgements

The authors would like to thank the reviewers for their insightful comments and suggestions which greatly improved the paper.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Salvatore Fasola
    • 1
  • Vito M. R. Muggeo
    • 1
  • Helmut Küchenhoff
    • 2
  1. 1.Department of Economical, Business and Statistical SciencesUniversity of PalermoPalermoItaly
  2. 2.Statistiches Beratungs Labor - Institut für StatistikUniversity of MunichMunichGermany

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