論文

2013年3月

Tuning parameter selection in sparse regression modeling

COMPUTATIONAL STATISTICS & DATA ANALYSIS
  • Kei Hirose
  • ,
  • Shohei Tateishi
  • ,
  • Sadanori Konishi

59
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
DOI
https://doi.org/10.1016/j.csda.2012.10.005
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000313082600003&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.csda.2012.10.005
  • ISSN : 0167-9473
  • Web of Science ID : WOS:000313082600003

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