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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Economical, Business and Statistical Sciences, University of Palermo, Viale delle Scienze, Edificio 13, 90128, Palermo, Italy

    Salvatore Fasola & Vito M. R. Muggeo

  2. Statistiches Beratungs Labor - Institut für Statistik, University of Munich, Munich, Germany

    Helmut Küchenhoff

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  1. Salvatore Fasola
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  2. Vito M. R. Muggeo
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  3. Helmut Küchenhoff
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Correspondence to Salvatore Fasola.

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Fasola, S., Muggeo, V.M.R. & Küchenhoff, H. A heuristic, iterative algorithm for change-point detection in abrupt change models. Comput Stat 33, 997–1015 (2018). https://doi.org/10.1007/s00180-017-0740-4

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  • DOI: https://doi.org/10.1007/s00180-017-0740-4

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