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

[摘要] 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.

[学科分类] 土木及结构工程学

[关键词] One-step estimator;oracle properties;penalized likelihood