Block thresholding wavelet regression using SCAD penalty |
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Authors: | Cheolwoo Park |
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Institution: | Department of Statistics, University of Georgia, GA 30602, USA |
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Abstract: | This paper concerns wavelet regression using a block thresholding procedure. Block thresholding methods utilize neighboring wavelet coefficients information to increase estimation accuracy. We propose to construct a data-driven block thresholding procedure using the smoothly clipped absolute deviation (SCAD) penalty. A simulation study demonstrates competitive finite sample performance of the proposed estimator compared to existing methods. We also show that the proposed estimator achieves optimal convergence rates in Besov spaces. |
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Keywords: | Besov space Block thresholding Convergence rates Smoothly clipped absolute deviation penalty Wavelet regression |
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