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Title
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Tuning parameter estimation in penalized least squares methodology
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Author
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Abstract
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| The efficiency of the penalized methods (Fan and Li, 2001) depends strongly on a tuning parameter due to the fact that it controls the extent of penalization. Therefore, it is important to select it appropriately. In general, tuning parameters are chosen by data-driven approaches, such as the commonly used generalized cross validation. In this article, we propose an alternative method for the derivation of the tuning parameter selector in penalized least squares framework, which can lead to an ameliorated estimate. Simulation studies are presented to support theoretical findings and a comparison of the Type I and Type II error rates, considering the L1, the hard thresholding and the Smoothly Clipped Absolute Deviation penalty functions, is performed. The results are given in tables and discussion follows. |
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Language
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English
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Source (journal)
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Communications in statistics : simulation and computation. - New York, N.Y.
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Publication
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New York, N.Y.
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2011
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ISSN
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0361-0918
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DOI
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10.1080/03610918.2011.575507
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Volume/pages
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40
:9
(2011)
, p. 1444-1457
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ISI
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000291275900008
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Full text (Publisher's DOI)
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