# Detecting a Structural Change in Functional Time Series Using Local Wilcoxon Statistic

arXiv: https://arxiv.org/abs/1604.03776 · HTML: https://mlsanity.com/paper/1604.03776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2016.

## Built on by (most cited first, 1 of 1)

- [Free congruence: an exploration of expanded similarity measures for time series data](https://mlsanity.com/paper/2101.08659/) (2021)

## Abstract (arXiv)

Functional data analysis (FDA) is a part of modern multivariate statistics that analyses data providing information about curves, surfaces or anything else varying over a certain continuum. In economics and empirical finance we often have to deal with time series of functional data, where we cannot easily decide, whether they are to be considered as homogeneous or heterogeneous. At present a discussion on adequate tests of homogenity for functional data is carried. We propose a novel statistic for detetecting a structural change in functional time series based on a local Wilcoxon statistic induced by a local depth function proposed by Paindaveine and Van Bever (2013).