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[预告]05.23 统计学院系列学术报告第7期——Scalable Kernel-based Variable Selection With Sparsistency

  报告题目:Scalable Kernel-based Variable Selection With Sparsistency

  

  报告人:王军辉

  

  报告人简介:王军辉教授现为香港城市大学数学系副教授兼副系主任,毕业于美国明尼苏达大学获统计学博士学位,并曾在美国哥伦比亚大学以及伊利诺伊大学芝加哥分校担任教职。研究方向包括统计机器学习,大规模文本数据挖掘,模型选择以及变量选择,并曾发表学术论文40余篇,包括数篇JASA,Biometrika,JMLR等顶尖的统计及机器学习杂志。

  

  报告时间:2018年5月23日(周三)15:30

  

  报告地点:统计学院办公楼104会议室

  

  报告摘要:Variable selection is central to sparse modeling, and many methods have been proposed under various model assumptions. In this talk, we will present a scalable framework for model-free variable selection in reproducing kernel Hilbert space (RKHS) without specifying any restrictive model. As opposed to most existing model-free variable selection methods requiring xed dimension, the proposed method allows dimension p to diverge with sample size n. The proposed method is motivated from the classical hard-threshold variable selection for linear models, but allows for general variable eects.It does not require specication of the underlying model for the response,which is appealing in sparse modeling with a large number of variables.The proposed method can also be adapted to various scenarios with specic model assumptions, including linear models, quadratic models, as well as additive models. The asymptotic estimation and variable selection consistencies of the proposed method are established in all the scenarios. If time permits, the extension of the proposed method beyond mean regression will also be discussed.

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