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Selection of Smoothing Parameter for One-Step Sparse Estimates with Lq Penalty
Volume 9, Issue 4 (2011), pp. 549–564
Masaru Kanba   Kanta Naito  

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https://doi.org/10.6339/JDS.201110_09(4).0005
Pub. online: 10 July 2021      Type: Research Article      Open accessOpen Access

Published
10 July 2021

Abstract

Abstract: This paper discusses the selection of the smoothing parameter necessary to implement a penalized regression using a nonconcave penalty function. The proposed method can be derived from a Bayesian viewpoint, and the resultant smoothing parameter is guaranteed to satisfy the sufficient conditions for the oracle properties of a one-step estimator. The results of simulation and application to some real data sets reveal that our proposal works efficiently, especially for discrete outputs.

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Keywords
One-step estimator oracle properties penalized likelihood

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Journal of data science

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