2017年06月20日，星期二下午14:50在大学城广州大学行政东楼前座412会议室。香港城市大学王军辉教授为各位学子带来了精彩的学术讲座--《An Efficient Framework for Variable Selection in Reproducing Kernel Hilbert Space》。
Variable selection is central to sparse modeling, and many methods have been proposed under various model assumptions. In this talk, we will present an efficient framework for model-free variable selection in reproducing kernel Hilbert space (RKHS) without specifying any restrictive model. As opposed to most existing model-free variable selection methods requiring fixed dimension, the proposed method allows dimension p to diverge at an exponential order of sample size n. The proposed method is motivated from the classical hard-threshold variable selection for linear models, but allows for general variable effects. It does not require specification of the underlying model for the response, which is appealing in sparse modeling with a large number of variables. The proposed method can also be adapted to various scenarios with specific model assumptions, including linear models, quadratic models, as well as additive models. The asymptotic estimation and variable selection consistencies of the proposed method are established in all the scenarios. If time permits, the extension of the proposed method beyond mean regression will also be discussed.