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

2020/09/11 by Piotr Fryźlewicz, Fryzlewicz, Piotr · 3 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2009.05431

openalex publication_date 2020/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localised 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 (see https://CRAN.R-project.org/package=nsp ).

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