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Abstract Details
Activity Number:
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309
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract - #301066 |
Title:
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On Data Adaptive Wavelet Decomposition and Bootstrap Under Long-Range Dependence
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Author(s):
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Jan Beran*+ and Yevgen Shumeyko
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Companies:
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University of Konstanz and University of Konstanz
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Address:
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Department of Mathematics and Statistics,, Konstanz, International, 78457, Germany
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Keywords:
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long-range dependence ;
wavelets ;
bootstrap ;
discontinuity ;
data adaptive ;
time series
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Abstract:
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Optimal data adaptive wavelet estimation of a trend function in time series with long-range dependence leads to a natural decomposition into a low and a high-resolution part. Explicit formulas for optimal decomposition levels and smoothing parameters are obtained. Moreover, a bootstrap test is developed to detect discontinuities in the underlying trend function. Asymptotic validity and consistency of the test are derived under general conditions.
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