Robust Change Detection in the Dependence Structure of Multivariate Time Series

Daniel Vogel () and Roland Fried ()
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Daniel Vogel: University of Aberdeen, Institute for Complex Systems and Mathematical Biology
Roland Fried: Technische Universität Dortmund, Fakultät Statistik

Chapter Chapter 16 in Modern Nonparametric, Robust and Multivariate Methods, 2015, pp 265-288 from Springer

Abstract: 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.

Keywords: GARCH; Near epoch dependence; Oja sign covariance matrix; Orthogonal invariance; Spatial sign covariance matrix; Tyler matrix (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-22404-6_16

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DOI: 10.1007/978-3-319-22404-6_16

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