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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>2013
<mark>Journal</mark>Electronic Journal of Statistics
Number of pages17
Pages (from-to)1167-1183
Publication StatusPublished
<mark>Original language</mark>English


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.