Robust Retrospective Multiple Change-point Estimation for Multivariate Data

TL;DR

This paper introduces a robust, non-parametric method for detecting multiple change-points in multivariate data, effective even with outliers, high noise, and unknown change-point counts, using a generalized Kruskal-Wallis statistic.

Contribution

It presents a distribution-free, parameter-free change-point detection technique for multidimensional signals that is computationally efficient and robust against atypical data distributions.

Findings

More robust than existing methods in simulations

Effective with high noise and outliers

Applicable to high-dimensional data

Abstract

We propose a non-parametric statistical procedure for detecting multiple change-points in multidimensional signals. The method is based on a test statistic that generalizes the well-known Kruskal-Wallis procedure to the multivariate setting. The proposed approach does not require any knowledge about the distribution of the observations and is parameter-free. It is computationally efficient thanks to the use of dynamic programming and can also be applied when the number of change-points is unknown. The method is shown through simulations to be more robust than alternatives, particularly when faced with atypical distributions (e.g., with outliers), high noise levels and/or high-dimensional data.