报告题目: Sure Explained Variability and Independence Screening

报 告 人：陈敏研究员 中科院

报告时间：2019年5月21日 14:30-16:00

报告地点：数学楼一楼第二报告厅

报告摘要：

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.

报告人简介：

  陈敏研究员现担任中国科学院政府行政管理系统分析研究中心主任、全国统计方法应用技术标准化委员会主任委员、《数学与统计管理》主编、中国数学学会副理事长、中国统计教育学会副会长等职，研究方向为金融统计理论与方法、非线性时间序列的统计分析、非参数统计估计和检验的大样本理论、生物统计的理论和方法、应用统计、大数据分析与处理的统计理论和算法研究。