报告主题: Sure Explained Variability and Independence Screening

报告人: Zhengjun Zhang, University of Wisconsin

报告时间：2017年7月7日（周五）下午3:30；

报告地点：北院卓远楼统计与数学学院305会议室；

摘要:

In the era of Big Data, extracting the most important exploratory variables available in ultrahigh-dimensional data plays a key role in scientific researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and independence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalized measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods. This is a joint work with Yimin Lian, Min Chen, Zhao Chen.

张正军教授简介：
云南财经大学统计与数学学院特聘教授，威斯康星大学统计系教授、副主任；北卡罗来纳大学教堂山分校统计学博士，北京航空航天大学管理工程博士。曾获得University of North Carolina教学奖等多项奖励，2010年入选剑桥名人录。主持有10余项美国自然科学基金等科研课题和美国国家卫生署的重大研究课题；在JASA、JoE等顶级统计学、经济学期刊发表学术论文50余篇。同时担任Journal of Business and Economic Statistics等多个国际著名统计学期刊的副主编。