Tuning parameter selection in sparse regression modeling

Kei Hirose, Shohei Tateishi, Sadanori Konishi

研究成果: ジャーナルへの寄稿記事

9 引用 (Scopus)

抜粋

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' Cp 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 Cp criterion based on our algorithm performs well in various situations.

元の言語英語
ページ(範囲)28-40
ページ数13
ジャーナルComputational Statistics and Data Analysis
59
発行部数1
DOI
出版物ステータス出版済み - 3 1 2013
外部発表Yes

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

これを引用