  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 ).
