Cookies?
Library Header Image
LSE Research Online LSE Library Services

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

Fryzlewicz, Piotr (2017) Tail-greedy bottom-up data decompositions and fast mulitple change-point detection. Annals of Statistics. ISSN 0090-5364 (In Press)

[img]
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
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 20 Nov 2017 11:40
Last Modified: 20 Nov 2017 11:40
URI: http://eprints.lse.ac.uk/id/eprint/85647

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics