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2013 A wavelet-based approach for detecting changes in second order structure within nonstationary time series
R. Killick, I. A. Eckley, P. Jonathan
Electron. J. Statist. 7: 1167-1183 (2013). DOI: 10.1214/13-EJS799

Abstract

This article proposes a test to detect changes in general autocovariance structure in nonstationary time series. Our approach is founded on the locally stationary wavelet (LSW) process model for time series which has previously been used for classification and segmentation of time series. Using this framework we form a likelihood-based hypothesis test and demonstrate its performance against existing methods on various simulated examples as well as applying it to a problem arising from ocean engineering.

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R. Killick. I. A. Eckley. P. Jonathan. "A wavelet-based approach for detecting changes in second order structure within nonstationary time series." Electron. J. Statist. 7 1167 - 1183, 2013. https://doi.org/10.1214/13-EJS799

Information

Published: 2013
First available in Project Euclid: 23 April 2013

zbMATH: 1337.62269
MathSciNet: MR3056071
Digital Object Identifier: 10.1214/13-EJS799

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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