A new sparse variable selection via random-effect model

JOURNAL OF MULTIVARIATE ANALYSIS

卷:125

Article

Lee, Youngjo¹  Oh, Hee-Seok¹ 

[1] Seoul Natl Univ, Dept Stat, Seoul 151747, South Korea

关键词: Maximum likelihood estimator;    Prediction;    Random-effect models;    Sparsity;    Variable selection;   

DOI  :  10.1016/j.jmva.2013.11.016

来源: Elsevier

【 摘 要 】

We study a new approach to simultaneous variable selection and estimation via random-effect models. Introducing random effects as the solution of a regularization problem is a flexible paradigm and accommodates likelihood interpretation for variable selection. This approach leads to a new type of penalty, unbounded at the origin and provides an oracle estimator without requiring a stringent condition. The unbounded penalty greatly enhances the performance of variable selections, enabling highly accurate estimations, especially in sparse cases. Maximum likelihood estimation is effective in enabling sparse variable selection. We also study an adaptive penalty selection method to maintain a good prediction performance in cases where the variable selection is ineffective. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free