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Abstract Details
Activity Number:
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443
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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International Chinese Statistical Association
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Abstract - #303612 |
Title:
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Segmenting Low- and High-Dimensional Time Series via Binary Segmentation and Its Modern Variants
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Author(s):
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Haeran Cho and Piotr Fryzlewicz*+
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Companies:
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London School of Economics and London School of Economics
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Address:
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Department of Statistics, Houghton Street, London, International, WC2A 2AE, United Kingdom
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Keywords:
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change-point detection ;
CUSUM ;
wavelets ;
time series ;
binary segmentation ;
high dimensionality
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Abstract:
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We propose a fast, well-performing, and consistent method for segmenting a piecewise-stationary, linear time series with an unknown number of change-points. 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 change-points 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 change-points in the second-order structure of the original process. An extensive simulation study demonstrates good performance of our method. We also present a more recent extension of our technique to a multivariate setting with a possibly large number of time series, as well as an improved "wild" version of the binary segmentation procedure.
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