Narrowest Significance Pursuit: inference for multiple change-points in linear models
Narrowest Significance Pursuit: inference for multiple change-points in linear models
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DOI：
10.1080/01621459.2023.2211733
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发表时间：
2020-09
影响因子：
3.7
通讯作者：
P. Fryzlewicz
中科院分区：
文献类型：
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作者：
P. Fryzlewicz
摘要我们提出了最窄意义追踪（NSP），一种通用而灵活的方法，用于自动检测数据序列中的局部区域，每个区域必须包含一个变点（理解为底层线性模型参数的突然变化），在规定的全局显著性水平。NSP的工作与广泛的分布假设的错误，并保证重要的随机边界，直接产生确切的期望的覆盖概率，无论形式或数量的回归。与广泛研究的“后选择推理”方法相比，NSP为“后推理选择”的概念铺平了道路。在R包nsp中有一个实现。本文的补充材料可在网上查阅。
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.