Journal article
Change point detection in high dimensional data with U-statistics
Test (Madrid, Spain), v 33(2), pp 400-452
01 Jun 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We consider the problem of detecting distributional changes in a sequence of high dimensional data. Our approach combines two separate statistics stemming from L-p norms whose behavior is similar under H-0 but potentially different under HA, leading to a testing procedure that that is flexible against a variety of alternatives. We establish the asymptotic distribution of our proposed test statistics separately in cases of weakly dependent and strongly dependent coordinates as min{N, d} -> infinity, where N denotes sample size and d is the dimension, and establish consistency of testing and estimation procedures in high dimensions under one-change alternative settings. Computational studies in single and multiple change point scenarios demonstrate our method can outperform other nonparametric approaches in the literature for certain alternatives in high dimensions. We illustrate our approach through an application to Twitter data concerning the mentions of U.S. governors.
Metrics
3 Record Views
1 citations in Scopus
Details
- Title
- Change point detection in high dimensional data with U-statistics
- Creators
- B. Cooper Boniece - Drexel University, MathematicsLajos Horvath - University of UtahPeter M. Jacobs - University of Utah
- Publication Details
- Test (Madrid, Spain), v 33(2), pp 400-452
- Publisher
- Springer Nature
- Number of pages
- 53
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:001115528200001
- Scopus ID
- 2-s2.0-85178920297
- Other Identifier
- 991021861289804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability