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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A wavelet-based approach for detecting changes in second order structure within nonstationary time series. / Killick, Rebecca; Eckley, Idris; Jonathan, Philip.
In: Electronic Journal of Statistics, Vol. 7, 2013, p. 1167-1183.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Killick, R, Eckley, I & Jonathan, P 2013, 'A wavelet-based approach for detecting changes in second order structure within nonstationary time series', Electronic Journal of Statistics, vol. 7, pp. 1167-1183. https://doi.org/10.1214/13-EJS799

APA

Killick, R., Eckley, I., & Jonathan, P. (2013). A wavelet-based approach for detecting changes in second order structure within nonstationary time series. Electronic Journal of Statistics, 7, 1167-1183. https://doi.org/10.1214/13-EJS799

Vancouver

Killick R, Eckley I, Jonathan P. A wavelet-based approach for detecting changes in second order structure within nonstationary time series. Electronic Journal of Statistics. 2013;7:1167-1183. doi: 10.1214/13-EJS799

Author

Killick, Rebecca ; Eckley, Idris ; Jonathan, Philip. / A wavelet-based approach for detecting changes in second order structure within nonstationary time series. In: Electronic Journal of Statistics. 2013 ; Vol. 7. pp. 1167-1183.

Bibtex

@article{a64d96037b734087b38875715de5f68f,
title = "A wavelet-based approach for detecting changes in second order structure within nonstationary time series",
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. ",
author = "Rebecca Killick and Idris Eckley and Philip Jonathan",
year = "2013",
doi = "10.1214/13-EJS799",
language = "English",
volume = "7",
pages = "1167--1183",
journal = "Electronic Journal of Statistics",
issn = "1935-7524",
publisher = "Institute of Mathematical Statistics",

}

RIS

TY - JOUR

T1 - A wavelet-based approach for detecting changes in second order structure within nonstationary time series

AU - Killick, Rebecca

AU - Eckley, Idris

AU - Jonathan, Philip

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1214/13-EJS799

DO - 10.1214/13-EJS799

M3 - Journal article

VL - 7

SP - 1167

EP - 1183

JO - Electronic Journal of Statistics

JF - Electronic Journal of Statistics

SN - 1935-7524

ER -