Speaker: Yan Xiaodong, Research Assistant, the School of Economics, Shandong University
Date: April 12, 2018
Time: 3:00 p.m.-5:00 p.m.
Location: Room 423, Block B, Zhixin Building, Central Campus
Sponsor: the School of Economics
Abstract: 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.
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Edited by: Wang Jingnan, Lang Cuicui