讲座题目：An overview of Spearman filter for feature screening
主 讲 人：严晓东 香港理工大学助理研究员
时 间：1月25日（周四）下午14:30
地 点：燕山校区1号教学楼5楼统计学院资料室
主讲人简介：
严晓东，香港理工大学助理研究员；于2017年获得云南大学博士；2006-2010就读于山东财经大学统计与数学学院； 2007.9-2009.6在山东大学交流访问一年；2014.10-2014.12在香港中文大学访问；2015.7-2017.12 在香港理工大学做全职研究助理； 2017.12年在香港理工大学做博士后至今。研究兴趣主要集中在高维数据的变量选择和特征筛选、缺失数据统计建模、贝叶斯局部影响分析、生存分析以及最近热门的子群分析、融合分析和深度学习。论文发表在诸如经济领域权威期刊Journal of Econometrics以及概率统计领域一流期刊 Computational Statistics & Data Analysis.
报告摘要：
In this presentation, we propose Spearman rank correlation based screening procedure for ultrahigh-dimensional data with complete, censored, missing or categorical response cases, respectively . The proposed method is model-free without specifying any regression form of predictors and a response variable and it is invariant to monotone transformations of a response variable and predictors. The sure screening and rank consistency properties are established under some mild regularity conditions. The simulation studies demonstrate that the new screening method performs well in the presence of the heavy-tailed distribution or strongly dependent predictors or outliers and that it has the superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring or missing rate. And when the response is categorical, it can deal with categorical-adaptive screening procedure. An illustrative example is provided.
科研处 统计学院（大数据与指数研究院）
山东省大数据研究会
2018年1月23日
