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

Author

Listed:
  • Salvatore Fasola

    (University of Palermo)

  • Vito M. R. Muggeo

    (University of Palermo)

  • Helmut Küchenhoff

    (University of Munich)

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.

Suggested Citation

  • Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0740-4
    DOI: 10.1007/s00180-017-0740-4
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    References listed on IDEAS

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    1. Klaus Frick & Axel Munk & Hannes Sieling, 2014. "Multiscale change point inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 495-580, June.
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    3. Ralph C A Rippe & Jacqueline J Meulman & Paul H C Eilers, 2012. "Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    4. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    5. Hawkins, Douglas M., 2001. "Fitting multiple change-point models to data," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 323-341, September.
    6. Guha, Subharup & Li, Yi & Neuberg, Donna, 2008. "Bayesian Hidden Markov Modeling of Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 485-497, June.
    7. Christopher H. Jackson & Linda D. Sharples, 2004. "Models for longitudinal data with censored changepoints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 149-162, January.
    8. Lavielle, Marc, 1999. "Detection of multiple changes in a sequence of dependent variables," Stochastic Processes and their Applications, Elsevier, vol. 83(1), pages 79-102, September.
    9. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
    10. Balke, Nathan S, 1993. "Detecting Level Shifts in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 81-92, January.
    11. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    1. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).

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