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 language | English |
---|---|
Title of host publication | Modern Nonparametric, Robust and Multivariate Methods |
Subtitle of host publication | Festschrift in Honour of Hannu Oja |
Editors | Klaus Nordhausen, Sara Taskinen |
Publisher | Springer International Publishing |
Pages | 265-288 |
Number of pages | 24 |
ISBN (Electronic) | 978-3-319-22404-6 |
ISBN (Print) | 978-3-319-22403-9 |
DOIs | |
Publication status | Published - 2015 |
Keywords
- GARCH
- near epoch dependence
- Oja sign covariance matrix
- orthogonal invariance
- spatial sign covariance matrix
- Tyler matrix