Electronic Journal of Statistics

A wavelet-based approach for detecting changes in second order structure within nonstationary time series

R. Killick, I. A. Eckley, and P. Jonathan

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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.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 1167-1183.

Dates
First available in Project Euclid: 23 April 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1366722164

Digital Object Identifier
doi:10.1214/13-EJS799

Mathematical Reviews number (MathSciNet)
MR3056071

Zentralblatt MATH identifier
1337.62269

Keywords
segmentation wavelets local stationarity significant wave height

Citation

Killick, R.; Eckley, I. A.; Jonathan, P. A wavelet-based approach for detecting changes in second order structure within nonstationary time series. Electron. J. Statist. 7 (2013), 1167--1183. doi:10.1214/13-EJS799. https://projecteuclid.org/euclid.ejs/1366722164


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