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Narrowest Significance Pursuit: inference for multiple change-points in linear models

Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2023) Narrowest Significance Pursuit: inference for multiple change-points in linear models. Journal of the American Statistical Association. ISSN 0162-1459

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Identification Number: 10.1080/01621459.2023.2211733

Abstract

We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localized regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. NSP works with a wide range of distributional assumptions on the errors, and guarantees important stochastic bounds which directly yield exact desired coverage probabilities, regardless of the form or number of the regressors. In contrast to the widely studied “post-selection inference” approach, NSP paves the way for the concept of “post-inference selection.” An implementation is available in the R package nsp. Supplementary materials for this article are available online.

Item Type: Article
Official URL: https://www.tandfonline.com/journals/uasa20
Additional Information: © 2023 The Author
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 04 May 2023 13:39
Last Modified: 16 May 2024 04:06
URI: http://eprints.lse.ac.uk/id/eprint/118795

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