کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
416824 | 681404 | 2013 | 13 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Tuning parameter selection in sparse regression modeling Tuning parameter selection in sparse regression modeling](/preview/png/416824.png)
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’ CpCp 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 CpCp criterion based on our algorithm performs well in various situations.
Journal: Computational Statistics & Data Analysis - Volume 59, March 2013, Pages 28–40