A Data-driven Change-point Estimator

TL;DR

This paper introduces an adaptive change-point estimator using data-driven weights to improve detection accuracy regardless of change location, ensuring valid change-point tests.

Contribution

It proposes a novel adaptive estimator for change-point detection that overcomes location dependency issues present in traditional methods.

Findings

The adaptive estimator improves detection accuracy across different change locations.

The proposed method maintains the validity of change-point tests.

Performance is demonstrated through theoretical validation.

Abstract

The q-weighted CUSUM and their corresponding estimator are well known statistics for change-point detection and estimation. They have the difficulty that the performance is highly dependent on the location of the change. An adaptive estimator with data-driven weights is presented to overcome this problem, and it is shown that the corresponding adaptive change-point tests are valid.