Block thresholding wavelet regression using SCAD penalty

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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. (C) 2010 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2010-09
Language
English
Article Type
Article
Citation

JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.140, no.9, pp.2755 - 2770

ISSN
0378-3758
DOI
10.1016/j.jspi.2010.03.035
URI
http://hdl.handle.net/10203/285780
Appears in Collection
MA-Journal Papers(저널논문)
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