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Activity Number:
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496
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #309295 |
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Title:
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On Block Thresholding in Wavelet Regression with Long Memory Correlated Noise
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Author(s):
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Linyuan Li*+
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Companies:
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University of New Hampshire
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Address:
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Department of Mathematics and Statistics, Durham, NH, 03824,
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Keywords:
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Block thresholding ; long range dependence data ; minimax estimation ; rates of convergence
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Abstract:
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We consider block thresholded wavelet-based estimators of mean regression functions with long memory Gaussian errors and investigate their asymptotic convergence rates. We show that these estimators, based on block thresholding of empirical wavelet coefficients, achieve optimal minimax convergence rates over a large range of Besov function classes. These results are analogous to those in the standard regression problem with i.i.d. Gaussian errors. Therefore, in the presence of long memory Gaussian errors, wavelet estimators still provide extensive adaptivity over a wide range of Besov classes.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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