报告简介  Abstract

Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorporates the joint effects among covariates into the screening process. With a data-driven threshold, the new method can automatically determine the number of features that should be retained after screening. These merits make the CD-based screening very appealing in practice. Under certain regularity conditions, we show that the proposed method possesses sure screening property. The performance of our proposal is illustrated through simulations and a real data example.

嘉宾简介  About the Speaker

朱利平教授，中国人民大学杰出学者特聘教授，中国人民大学统计与大数据研究院教授、博士生导师。获国家自然科学基金委员会优秀青年基金资助，并入选教育部新世纪优秀人才计划、中组部青年拔尖人才万人计划以及中宣部文化名家暨“四个一批”人才计划。朱利平教授2006年于华东师范大学取得博士学位。一直从事复杂数据分析的统计理论、方法及应用研究工作。研究兴趣主要涉及复杂“高维”数据分析以及复杂“非线性相依”数据分析。曾是《The Annals of Statistics》，《Statistica Sinica》等国际重要学术期刊的Associate Editor。目前是《Journal of Multivariate Analysis》, 《Statistics and Its Interface》等SCI期刊的Associate Editor，以及《系统科学与数学》和《应用概率统计》期刊编委。

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