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2011 Testing the structural stability of temporally dependent functional observations and application to climate projections
Xianyang Zhang, Xiaofeng Shao, Katharine Hayhoe, Donald J. Wuebbles
Electron. J. Statist. 5: 1765-1796 (2011). DOI: 10.1214/11-EJS655

Abstract

We develop a self-normalization (SN) based test to test the structural stability of temporally dependent functional observations. Testing for a change point in the mean of functional data has been studied in Berkes, Gabrys, Horváth and Kokoszka [4], but their test was developed under the independence assumption. In many applications, functional observations are expected to be dependent, especially when the data is collected over time. Building on the SN-based change point test proposed in Shao and Zhang [23] for a univariate time series, we extend the SN-based test to the functional setup by testing the constant mean of the finite dimensional eigenvectors after performing functional principal component analysis. Asymptotic theories are derived under both the null and local alternatives. Through theory and extensive simulations, our SN-based test statistic proposed in the functional setting is shown to inherit some useful properties in the univariate setup: the test is asymptotically distribution free and its power is monotonic. Furthermore, we extend the SN-based test to identify potential change points in the dependence structure of functional observations. The method is then applied to central England temperature series to detect the warming trend and to gridded temperature fields generated by global climate models to test for changes in spatial bias structure over time.

Citation

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Xianyang Zhang. Xiaofeng Shao. Katharine Hayhoe. Donald J. Wuebbles. "Testing the structural stability of temporally dependent functional observations and application to climate projections." Electron. J. Statist. 5 1765 - 1796, 2011. https://doi.org/10.1214/11-EJS655

Information

Published: 2011
First available in Project Euclid: 13 December 2011

zbMATH: 1271.62097
MathSciNet: MR2870150
Digital Object Identifier: 10.1214/11-EJS655

Subjects:
Primary: 62G10
Secondary: 62G20

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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