High-dimensional, multiscale online changepoint detection

Yudong Chen, Tengyao Wang and Richard J. Samworth

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: average run length; detection delay; high-dimensional changepoint detection; online algorithm; sequential method (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2022-02-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (4)

Published in Journal of the Royal Statistical Society. Series B: Statistical Methodology, 1, February, 2022, 84(1), pp. 234 - 266. ISSN: 1369-7412

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:113665

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