抄録
In sparse regression modeling via regularization such as the lasso, elastic net and bridge regression, 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 criterion may be used to choose the tuning parameters, for which the concept of degrees of freedom plays a key role. In the present paper, we propose an efficient algorithm which computes the degrees of freedom sequentially by extending the generalized path seeking algorithm. Monte Carlo simulations demonstrate that our methodology performs well in various situations.
