Robust Narrowest Significance Pursuit: inference for multiple change-points in the median

Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2024) Robust Narrowest Significance Pursuit: inference for multiple change-points in the median. Journal of Business and Economic Statistics, 42 (4). pp. 1389-1402. ISSN 0735-0015

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Abstract

We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level. RNSP works by fitting the postulated constant model over many regions of the data using a new sign-multiresolution sup-norm-type loss, and greedily identifying the shortest intervals on which the constancy is significantly violated. By working with the signs of the data around fitted model candidates, RNSP fulfils its coverage promises under minimal assumptions, requiring only sign-symmetry and serial independence of the signs of the true residuals. In particular, it permits their heterogeneity and arbitrarily heavy tails. The intervals of significance returned by RNSP have a finite-sample character, are unconditional in nature and do not rely on any assumptions on the true signal. Code implementing RNSP is available at https://github.com/pfryz/nsp.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/ubes20
Additional Information:	© 2024 The Author
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory
Date Deposited:	05 Feb 2024 10:18
Last Modified:	15 Nov 2024 03:09
URI:	http://eprints.lse.ac.uk/id/eprint/121646

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