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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Original languageEnglish
Number of pages19
JournalComputational Statistics
Publication statusPublished - 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics


Dive into the research topics of 'A heuristic, iterative algorithm for change-point detection in abrupt change models'. Together they form a unique fingerprint.

Cite this