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Chenlu Ke, Ph.D. 
Assistant Professor
Department of Statistical Sciences and Operations Research 
Virginia Commonwealth University

In this talk, we first introduce a model-free sufficient variable selection procedure for ultrahigh dimensional data based on a newly developed independence measure. Compared with sure independence screening and other related methods, our approach inherits the power of the new measure and incorporates joint information between variables additionally to achieve sufficient variable selection. The advantages of our method are illustrated theoretically and numerically. In the second part of the talk, we introduce a novel sufficient dimension reduction approach using the same independence measure. An algorithm is developed to search dimension reduction directions using sequential quadratic programming. The method can be applied after our variable selection procedure to further extract information from data. A real data example is presented to demonstrate the joint use of the two methods.