王启华研究员 学术报告

发布时间:2017年11月16日 作者:唐颖   消息来源:业务办    阅读次数:[]

报告人:王启华研究员 中国科学院

报告时间:2017年11月17日下午16:30 -17:30

报告地点:数学与统计学院143室

简介:王启华,中国科学院核心骨干特聘研究员,博士生导师,国家杰出青年基金获得者,教育部长江学者奖励计划特聘教授,中科院“百人计划”入选者,国际统计研究会当选会员(elected member), 先后访问加拿大Carleton大学、California大学戴维斯分校、California大学洛杉矶分校、美国Yale大学、美国华盛顿大学、美国西北大学、德国Humboldt大学、澳大利亚国立大学及澳大利亚悉尼大学等。主要从事生存分析、缺失数据分析、高维数据统计分析及非-半参数统计推断等方面的研究。)出版专著两部,发表论文百余篇,其中90多篇发表在 The Annals of Statistics, JASA及Biometrika等国际重要刊物, 2014,2015及2016连续3年被Elsevier列入中国高被引专家, 是一些国际与国内刊物的主编与编委。

Simultaneous variable selection and class fusion with penalized distance criterion based classifiers

In this paper, we propose two new methods to solve the problem of constructing sparse multiclass classifiers and determining corresponding discriminative variables for each pair of classes simultaneously in the high-dimensional setting. In contrast to many existing multiclass classifiers, which can only select informative variables for classification, we can understand roles of the selected variables in separating particular pairs of classes more profoundly by using different penalties. Different from Guo (2010, Biostatistics) and Xu et al. (2015, Biometrika), which are based on the separate estimation of the precision matrix and mean vectors, we propose to construct classifiers by estimating products of the precision matrix and mean vectors or all discriminant directions directly with more appropriate penalties. This leads to the use of the distance criterion instead of the log-likelihood used in existing literature. With the proposed methods, we can not only consistently select informative variables for classification but also consistently identify corresponding discriminative variables for each pair of classes. More importantly, our methods attain asymptotically the optimal misclassification error rate for multiclass classification problems, which is not investigated in Guo (2010) and Xu et al. (2015). Simulations and the real data analysis well demonstrate good performances of our methods in comparison with existing methods.



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