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Robust Change Detection in the Dependence Structure of Multivariate Time Series

In: Modern Nonparametric, Robust and Multivariate Methods

Author

Listed:
  • Daniel Vogel

    (University of Aberdeen, Institute for Complex Systems and Mathematical Biology)

  • Roland Fried

    (Technische Universität Dortmund, Fakultät Statistik)

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 L 2 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.

Suggested Citation

  • Daniel Vogel & Roland Fried, 2015. "Robust Change Detection in the Dependence Structure of Multivariate Time Series," Springer Books, in: Klaus Nordhausen & Sara Taskinen (ed.), Modern Nonparametric, Robust and Multivariate Methods, edition 1, chapter 0, pages 265-288, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-22404-6_16
    DOI: 10.1007/978-3-319-22404-6_16
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