(5) Robust change detection in the dependence structure of multivariate time series

Daniel Vogel, Roland Fried

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

A robust change-point test based on the spatial sign covariance matrix is proposed. A major advantage of the test is its computational simplicity, making it particularly appealing for robust, high-dimensional data analysis. We derive the asymptotic distribution of the test statistic for stationary sequences, which we allow to be near-epoch dependent in probability (P NED) with respect to an α-mixing process. Contrary to the usual L2 near-epoch dependence, this short-range dependence condition requires no moment assumptions, and includes arbitrarily heavy-tailed processes. Further, we give a short review of the spatial sign covariance matrix and compare our test to a similar one based on the sample covariance matrix in a simulation study
Original languageEnglish
Title of host publicationModern Nonparametric, Robust and Multivariate Methods
Subtitle of host publicationFestschrift in Honour of Hannu Oja
EditorsKlaus Nordhausen, Sara Taskinen
PublisherSpringer International Publishing
Pages265-288
Number of pages24
ISBN (Electronic)978-3-319-22404-6
ISBN (Print)978-3-319-22403-9
DOIs
Publication statusPublished - 2015

Keywords

  • GARCH
  • near epoch dependence
  • Oja sign covariance matrix
  • orthogonal invariance
  • spatial sign covariance matrix
  • Tyler matrix

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    Vogel, D., & Fried, R. (2015). (5) Robust change detection in the dependence structure of multivariate time series. In K. Nordhausen, & S. Taskinen (Eds.), Modern Nonparametric, Robust and Multivariate Methods: Festschrift in Honour of Hannu Oja (pp. 265-288). Springer International Publishing. https://doi.org/10.1007/978-3-319-22404-6_16