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December 2015 Uniform change point tests in high dimension
Moritz Jirak
Ann. Statist. 43(6): 2451-2483 (December 2015). DOI: 10.1214/15-AOS1347


Consider $d$ dependent change point tests, each based on a CUSUM-statistic. We provide an asymptotic theory that allows us to deal with the maximum over all test statistics as both the sample size $n$ and $d$ tend to infinity. We achieve this either by a consistent bootstrap or an appropriate limit distribution. This allows for the construction of simultaneous confidence bands for dependent change point tests, and explicitly allows us to determine the location of the change both in time and coordinates in high-dimensional time series. If the underlying data has sample size greater or equal $n$ for each test, our conditions explicitly allow for the large $d$ small $n$ situation, that is, where $n/d\to0$. The setup for the high-dimensional time series is based on a general weak dependence concept. The conditions are very flexible and include many popular multivariate linear and nonlinear models from the literature, such as ARMA, GARCH and related models. The construction of the tests is completely nonparametric, difficulties associated with parametric model selection, model fitting and parameter estimation are avoided. Among other things, the limit distribution for $\max_{1\leq h\leq d}\sup_{0\leq t\leq1}\vert \mathcal{W}_{t,h}-t\mathcal{W}_{1,h}\vert $ is established, where $\{\mathcal{W}_{t,h}\}_{1\leq h\leq d}$ denotes a sequence of dependent Brownian motions. As an application, we analyze all S&P 500 companies over a period of one year.


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Moritz Jirak. "Uniform change point tests in high dimension." Ann. Statist. 43 (6) 2451 - 2483, December 2015.


Received: 1 August 2014; Revised: 1 April 2015; Published: December 2015
First available in Project Euclid: 7 October 2015

zbMATH: 1327.62467
MathSciNet: MR3405600
Digital Object Identifier: 10.1214/15-AOS1347

Primary: 62G32, 62M10
Secondary: 60F05, 60K35

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.43 • No. 6 • December 2015
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