High-dimensional, multiscale online changepoint detection

Chen, Yudong ORCID: 0000-0002-3034-4651, Wang, Tengyao ORCID: 0000-0003-2072-6645 and Samworth, Richard J. (2022) High-dimensional, multiscale online changepoint detection. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 84 (1). 234 - 266. ISSN 1369-7412

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Abstract

We introduce a new method for high-dimensional, online changepoint detection in settings where a p-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worst-case computational complexity per new observation are independent of the number of previous observations; in practice, it may even be significantly faster than this. We prove that the patience, or average run length under the null, of our procedure is at least at the desired nominal level, and provide guarantees on its response delay under the alternative that depend on the sparsity of the vector of mean change. Simulations confirm the practical effectiveness of our proposal, which is implemented in the R package ocd, and we also demonstrate its utility on a seismology data set.

Item Type:	Article
Official URL:	https://rss.onlinelibrary.wiley.com/journal/146798...
Additional Information:	© 2022 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	08 Feb 2022 11:15
Last Modified:	20 Dec 2024 00:43
URI:	http://eprints.lse.ac.uk/id/eprint/113665

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