论文标题
最窄的意义追求：线性模型中多个变更点的推断
Narrowest Significance Pursuit: inference for multiple change-points in linear models
论文作者
论文摘要
我们提出了最狭窄的意义Pursuit（NSP），这是一种通用和灵活的方法，用于自动检测数据序列中的局部区域，每个方法都必须包含一个变化点（理解为在规定的全球意义水平上的基础线性模型参数的突然变化）。 NSP在误差上使用各种分布假设，并保证重要的随机界限，这些随机界限直接产生确切的所需覆盖率概率，而与回归器的形式或数量无关。与已广泛研究的“选择后推断”方法相反，NSP为“后推导选择”的概念铺平了道路。 R Package NSP中提供了实现（请参阅https://cran.r-project.org/package=nsp）。
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 ).
