Narrowest Significance Pursuit: Inference for Multiple Change-Points in Linear Models

• 成果类型: Article
• 署名作者: Fryzlewicz, Piotr
• 署名单位: University of London; London School Economics & Political Science; University of London; London School Economics & Political Science
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2211733
• 发表日期: 2024
• 页码: 1633-1646
• 关键词: segmentation

摘要

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. for this article are available online.