Selection of Smoothing Parameter for One-Step Sparse Estimates with Lq Penalty

10.6339/JDS.2011.09(4).945

Masaru Kanba；Kanta Naito

One-step estimator ； oracle properties ； penalized likelihood ； smoothing parameter ； variable selection

Journal of Data Science

9卷4期（2011 / 10 / 01）

549 - 564

英文

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.