Robust Change-Point Detection for Functional Time Series Based on $U$-Statistics and Dependent Wild Bootstrap

TL;DR

This paper introduces a robust change-point detection method for functional time series using $U$-statistics and a dependent wild bootstrap, improving sensitivity to outliers and leveraging full functional data.

Contribution

It generalizes the Wilcoxon two-sample test to functional data and develops a new bootstrap method for asymptotic distribution approximation under dependence.

Findings

The proposed test is less sensitive to outliers than classical methods.

A new limit theorem for $U$-statistics in Hilbert spaces under dependence.

Effective critical value computation via a novel dependent wild bootstrap.

Abstract

The aim of this paper is to develop a change-point test for functional time series that uses the full functional information and is less sensitive to outliers compared to the classical CUSUM test. For this aim, the Wilcoxon two-sample test is generalized to functional data. To obtain the asymptotic distribution of the test statistic, we proof a limit theorem for a process of $U$-statistics with values in a Hilbert space under weak dependence. Critical values can be obtained by a newly developed version of the dependent wild bootstrap for non-degenerate 2-sample $U$-statistics.