Detecting multiple generalized change-points by isolating single ones

Anastasiou, Andreas and Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2022) Detecting multiple generalized change-points by isolating single ones. Metrika, 85 (2). 141 - 174. ISSN 0026-1335

Text (Anastasiou-Fryzlewicz2021_Article_DetectingMultipleGeneralizedCh) - Published Version Available under License Creative Commons Attribution. Download (832kB)

• Author
• Scopus publication

Abstract

We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

Item Type:	Article
Official URL:	https://www.springer.com/journal/184
Additional Information:	© 2021 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	28 Apr 2021 10:12
Last Modified:	16 Nov 2024 03:30
URI:	http://eprints.lse.ac.uk/id/eprint/110258

Actions (login required)

View Item