Tail-greedy bottom-up data decompositions and fast mulitple change-point detection

Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2018) Tail-greedy bottom-up data decompositions and fast mulitple change-point detection. Annals of Statistics, 46 (6B). pp. 3390-3421. ISSN 0090-5364

Preview

Text - Accepted Version Download (643kB) | Preview

Abstract

This article proposes a ‘tail-greedy’, bottom-up transform for one-dimensional data, which results in a nonlinear but conditionally orthonormal, multiscale decomposition of the data with respect to an adaptively chosen Unbalanced Haar wavelet basis. The ‘tail-greediness’of the decomposition algorithm, whereby multiple greedy steps are taken in a single pass through the data, both enables fast computation and makes the algorithm applicable in the problem of consistent estimation of the number and locations of multiple changepoints in data. The resulting agglomerative change-point detection method avoids the disadvantages of the classical divisive binary segmentation, and offers very good practical performance. It is implemented in the R package breakfast, available from CRAN.

Item Type:	Article
Official URL:	http://www.imstat.org/aos/
Additional Information:	© 2017 The Author
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics
Date Deposited:	20 Nov 2017 11:40
Last Modified:	11 Dec 2024 21:32
URI:	http://eprints.lse.ac.uk/id/eprint/85647

Actions (login required)

View Item