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

Understand

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 ).