Change point detection in high dimensional data with U-statistics

• B. Cooper Boniece ^[1] ; Lajos Horváth ^[2] ; Peter M. Jacobs ^[2]
  1. [1] Drexel University

     Drexel University

     City of Philadelphia, Estados Unidos

     
  2. [2] University of Utah

     University of Utah

     Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 33, Nº. 2, 2024, págs. 400-452
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 H_A, 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\}\rightarrow \infty , 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.