Discussion on “Model Confidence Bounds for Variable Selection” by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin

Li, Luo, Ferrari, Hui and Qin (hereafter referred to as Li et al.) have worked on an important theme. With the growing use of variable selection methods for model search and data mining, it is important for the users to be aware of the variability involved with the use of such procedures. Since data sets might be large and might consists of a large number of variables, it is important to have fast methods that compute such model uncertainty quantifications.