報告題目: Sure Explained Variability and Independence Screening
報 告 人：陳敏研究員 中科院
報告時間：2019年5月21日 14:30-16:00
報告地點：數學樓一樓第二報告廳
報告摘要：
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.
報告人簡介：
陳敏研究員現擔任中國科學院政府行政管理系統分析研究中心主任、全國統計方法應用技術标準化委員會主任委員、《數學與統計管理》主編、中國數學學會副理事長、中國統計教育學會副會長等職，研究方向為金融統計理論與方法、非線性時間序列的統計分析、非參數統計估計和檢驗的大樣本理論、生物統計的理論和方法、應用統計、大數據分析與處理的統計理論和算法研究。
