Cookies?
Library Header Image
LSE Research Online LSE Library Services

Inference in high-dimensional online changepoint detection

Chen, Yudong ORCID: 0000-0002-3034-4651, Wang, Tengyao ORCID: 0000-0003-2072-6645 and Samworth, Richard J. (2023) Inference in high-dimensional online changepoint detection. Journal of the American Statistical Association. ISSN 0162-1459

[img] Text (Inference in High Dimensional Online Changepoint Detection) - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB)

Identification Number: 10.1080/01621459.2023.2199962

Abstract

We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. The corresponding support estimate enjoys control of both false negatives and false positives. Simulations confirm the effectiveness of our methodology, and we also illustrate its applicability on the U.S. excess deaths data from 2017 to 2020. The supplementary material, which contains the proofs of our theoretical results, is available online.

Item Type: Article
Official URL: https://www.tandfonline.com/journals/uasa20
Additional Information: © 2023 The Author(s).
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 20 Jun 2023 12:15
Last Modified: 27 Feb 2024 23:00
URI: http://eprints.lse.ac.uk/id/eprint/119449

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics