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Abstract Details
Activity Number:
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545
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #300004 |
Title:
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A Self-Normalized Approach to Testing for Change Points in Time Series
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Author(s):
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Xiaofeng Shao*+
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Companies:
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University of Illinois at Urbana-Champaign
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Address:
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Department of Statistics, Champaign, IL, 61820, USA
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Keywords:
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change point ;
cusum ;
invariance principle ;
self-normalization
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Abstract:
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In this talk, we introduce a new class of change point test statistics in the time series setting. To test for a mean shift, the traditional Kolmogorov-Smirnov CUSUM-based test statistic involves a consistent long run variance estimator, which is needed to make the limiting null distribution free of nuisance parameters. The commonly used long run variance estimator requires to choose a bandwidth parameter and its selection is a difficult task in practice. The bandwidth that is a fixed function of the sample size is not adaptive to the magnitude of the dependence in the series, whereas the data-dependent bandwidth could lead to nonmonotonic power. To circumvent the difficulty, we propose a self-normalization (SN) based Kolmogorov-Smirnov test, where the formation of the self-normalizer takes the change point alternative into account. The resulting test statistic is asymptotically distr
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Authors who are presenting talks have a * after their name.
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