Inference for Multiple Change-Points in Linear Models
Description
Implementation of Narrowest Significance Pursuit, 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.
Narrowest Significance Pursuit works with a wide range of distributional assumptions
on the errors, and yields exact desired finite-sample coverage probabilities,
regardless of the form or number of the covariates. For details, see P. Fryzlewicz
(2021) .