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Category-Adaptive Variable Screening for Ultra-high Dimensional Heterogeneous Categorical Data
时间:2019年11月07日 14:07 点击数:

报告人:唐年胜

报告地点:数学与统计学院415室

报告时间:2019年11月09日星期六15:00-16:00

邀请人:郭建华、郑术蓉

报告摘要:

The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure with high-dimensional heterogeneous data, which is to detect category-specific important covariates. The proposal is a model-free approach without any specification of a regression model and an adaptive procedure in the sense that the set of active variables is allowed to vary across different categories, thus making it more flexible to accommodate heterogeneity.

For response-selective sampling data, another main discovery of this paper is that the proposed method works directly without any modification. Under mild regularity conditions, the newly procedure is shown to possess the sure screening and ranking consistency properties. Simulation studies contain supportive evidence that the proposed method performs well under various settings and it is effective to extract category-specific information. Applications are illustrated with two real data sets.

主讲人简介:

唐年胜,云南大学数学与统计学院院长。2007年入选教育部新世纪优秀人才支持计划;2012年获国家杰出青年科学基金、获“云南省中青年学术和技术带头人”称号;2013年入选“长江学者”奖励计划特聘教授;2014年入选云南省首批“云岭学者”和“省委联系专家”;2015年入选云南省高等学校教学名师、云南省科技领军人才、国家百千万人才工程,获“国家有突出贡献中青年专家”荣誉称号;2016年12月当选为Elected ISI Member(国际统计学会推选会员);2016年享受国务院政府特殊津贴;2017年当选国际泛华统计学会“Board of Directors”

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