2013年3月
Tuning parameter selection in sparse regression modeling
COMPUTATIONAL STATISTICS & DATA ANALYSIS
- ,
- ,
- 巻
- 59
- 号
- 開始ページ
- 28
- 終了ページ
- 40
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.csda.2012.10.005
- 出版者・発行元
- ELSEVIER SCIENCE BV
In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows' C-p type criteria may be used as a tuning parameter selection tool in lasso type regularization methods, for which the concept of degrees of freedom plays a key role. In the present paper, we propose an efficient algorithm that computes the degrees of freedom by extending the generalized path seeking algorithm. Our procedure allows us to construct model selection criteria for evaluating models estimated by regularization with a wide variety of convex and nonconvex penalties. The proposed methodology is investigated through the analysis of real data and Monte Carlo simulations. Numerical results show that C-p criterion based on our algorithm performs well in various situations. (C) 2012 Elsevier B.V. All rights reserved.
- リンク情報
- ID情報
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- DOI : 10.1016/j.csda.2012.10.005
- ISSN : 0167-9473
- Web of Science ID : WOS:000313082600003