Heterogeneous Change Point Inference

TL;DR

This paper introduces HSMUCE, a new method for detecting multiple change-points in heterogeneous Gaussian signals, which adapts locally to variance changes and provides accurate, non-asymptotic confidence sets.

Contribution

HSMUCE is a novel multiscale change-point detection method that controls error rates and adapts to heterogeneity in variance, with proven optimal detection rates.

Findings

HSMUCE controls over- and underestimation errors effectively.

It achieves near-optimal detection rates for small signals.

The method is computationally efficient and robust in heterogeneous settings.

Abstract

We propose HSMUCE (heterogeneous simultaneous multiscale change-point estimator) for the detection of multiple change-points of the signal in a heterogeneous gaussian regression model. A piecewise constant function is estimated by minimizing the number of change-points over the acceptance region of a multiscale test which locally adapts to changes in the variance. The multiscale test is a combination of local likelihood ratio tests which are properly calibrated by scale dependent critical values in order to keep a global nominal level alpha, even for finite samples. We show that HSMUCE controls the error of over- and underestimation of the number of change-points. To this end, new deviation bounds for F-type statistics are derived. Moreover, we obtain confidence sets for the whole signal. All results are non-asymptotic and uniform over a large class of heterogeneous change-point models. HSMUCE is fast to compute, achieves the optimal detection rate and estimates the number of change-points at almost optimal accuracy for vanishing signals, while still being robust. We compare HSMUCE with several state of the art methods in simulations and analyse current recordings of a transmembrane protein in the bacterial outer membrane with pronounced heterogeneity for its states. An R-package is available online.