题　目：Degrees of Freedom of the Reduced Rank Regression 主讲人：王乃昕教授 (University of Michigan, USA) 时　间：101年12月28日(星期五)上午10:40-11:30 地　点：交大综合一馆427室 Abstract We study the degrees of freedom of the reduced rank regression estimator in the framework of Stein's unbiased risk estimation (SURE). We derive a finite-sample exact unbiased estimator of the degrees of freedom for the reduced rank regression. We show that it is significantly different from the number of free parameters in the model, which is often taken as a heuristic estimate of the degrees of freedom for the reduced rank regression. Using the exact unbiased estimator of the degrees of freedom, one can easily employ various model selection criteria such as Mallow's Cp or GCV to efficiently choose an optimal rank for the reduced rank regression problem, which often outperforms its heuristic counterpart in terms of prediction accuracy and successfully avoids computationally expensive data perturbation or bootstrap based methods. We have also extended the proposed approach to other related estimation procedures, including the reduced rank ridge regression and a weighted nuclear norm penalized multivariate regression. --

※ 发信站: 批踢踢实业坊(ptt.cc) ◆ From: 140.113.46.59