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 ).

翻译：我们提出最窄显著性追踪（NSP）方法，这是一种通用且灵活的方法，用于自动检测数据序列中必须包含变点（理解为底层线性模型参数的突变）的局部区域，并满足预设的全局显著性水平。NSP适用于广泛的误差分布假设，并保证重要的随机界，这些界直接产生精确的期望覆盖概率，无论回归变量的形式或数量如何。与广泛研究的"后选择推断"方法不同，NSP为"后推断选择"概念铺平了道路。该方法的实现可在R包nsp中获取（参见https://CRAN.R-project.org/package=nsp）。