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Multiscale and multilevel technique for consistent segmentation of nonstationary time series

Cho, Haeran and Fryzlewicz, Piotr (2012) Multiscale and multilevel technique for consistent segmentation of nonstationary time series. Statistica Sinica, 22 (1). pp. 207-229. ISSN 1017-0405

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Abstract

In this paper, we propose a fast, well-performing, and consistent method for segmenting a piecewise-stationary, linear time series with an unknown number of breakpoints. The time series model we use is the nonparametric Locally Stationary Wavelet model, in which a complete description of the piecewise-stationary second-order structure is provided by wavelet periodograms computed at multiple scales and locations. The initial stage of our method is a new binary segmentation procedure, with a theoretically justified and rapidly computable test criterion that detects breakpoints in wavelet periodograms separately at each scale. This is followed by within-scale and across-scales post-processing steps, leading to consistent estimation of the number and locations of breakpoints in the second-order structure of the original process. An extensive simulation study demonstrates good performance of our method.

Item Type: Article
Official URL: http://www3.stat.sinica.edu.tw/statistica/
Additional Information: © 2012 Institute of Statistical Science, Academia Sinica
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 16 Apr 2012 11:49
URL: http://eprints.lse.ac.uk/43104/

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