(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)


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
Number of pages24
ISBN (Electronic)978-3-319-22404-6
ISBN (Print)978-3-319-22403-9
Publication statusPublished - 2015


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


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