6533b85ffe1ef96bd12c13ae
RESEARCH PRODUCT
A heuristic, iterative algorithm for change-point detection in abrupt change models
Salvatore FasolaHelmut KüchenhoffVito M. R. Muggeosubject
0301 basic medicineStatistics and ProbabilityMathematical optimizationIterative methodHeuristic (computer science)Linear model01 natural sciencesPiecewise constant model Approximate maximum likelihood Model linearization Grid search limitations010104 statistics & probability03 medical and health sciencesComputational MathematicsDiscontinuity (linguistics)030104 developmental biologyHyperparameter optimizationCovariatePiecewise0101 mathematicsStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaChange detectionMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-06-10 |