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统计青年专家报告:山东大学严晓东博士学术报告
发布日期:2018-05-02 浏览次数:

报告题目:Fused Mean-variance Filter for Feature Screening

报告人:严晓东博士

报告摘要:A new model-free screening approach called as the slicing fused mean-variance filter is proposed for ultrahigh dimensional data analysis. The new method has the following merits: (i) its implementation does not require specifying a regression form of predictors and response variables; (ii) it can deal with various types of covariates and response variables including continuous, discrete and categorical variables; (iii) it works well even when the covariates/random errors are heavy-tailed, or the predictors are strongly correlated, or there are outliers. Under some regularity conditions, the sure screening and ranking consistency properties are established for the proposed procedure without assuming any moment conditions on the predictors. Simulation studies are conducted to investigate the finite sample performance of the proposed procedure. A real data example is illustrated to the proposed procedure.

报告时间:57日上午10:3011:30

报告地点:统计学院213会议室

报告人简介:严晓东,山东大学经济学院助理研究员,香港理工大学研究助理。于2017年获得云南大学博士;2014.10-2014.12在香港中文大学访问;2015.7-2018.4 在香港理工大学做全职研究助理;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 Econometric》以及概率统计领域一流期刊《Computational Statistics & Data Analysis》,《Science China Mathematics》等。

 


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