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Southwest Jiaotong University School of Mathematics

统计系

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新加坡国立大学张金廷教授学术报告

欧洲杯买球网站:   作者:黄磊     日期:2019-12-04 13:46:02   点击数:  

人:张金廷教授

报告时间:2019129日(周一)15:00--16:00

报告地点:X2511会议室

主讲人简介:新加坡国立大学概率统计系终身教授,博士生、博士后导师,华侨大学福建省闽江学者讲座教授。早年于北京大学取得学士学位,中国科学院应用数学所取得硕士学位,美国北卡罗来纳大学教堂山分校获得博士学位,美国哈佛大学博士后。先后在美国普林斯顿、罗彻斯特等大学做高级访问学者。张教授培养了数十名硕士和七位博士以及三位博士后,其主要学术成果发表在Annals of Statistics(世界统计年刊),JASA(美国统计学会杂志),JRSSB(英国皇家统计学会杂志),Statistics Sinica(统计学报)等统计学国际顶级期刊上,著有统计学专著《Analysis of Variance for Functional Data》和《Nonparametric  Regression Methods for Longitudinal Data Analysis》,以及一本学术论文集。现任和曾任多家学术期刊的编委,并多次担任大型国际会议的组织委员。张教授现在的研究领域包括非参数统计,纵向数据分析,函数数据分析,高维数据分析等。

报告题目:A new knn-classifier for functional data with applications

报告摘要:In this talk, we discuss a new knn (k-nearest neighbors) classifier for functional data. For supervised classification of functional data, several classifiers have been proposed in the literature, including the well-known classic knn classifier. The classic knn classifier selects k nearest neighbors around a new observation and determines its class-membership according to a majority vote. A difficulty arises when there are two classes having the same largest number of votes.To overcome this difficulty, we propose a new knn classifier which selects k nearest neighbors around a new observation from each class. The class-membership of the new observation is determined by the minimum average distance or semi-distance between the k nearest neighbors and the new observation. Good performance of the new knnclassifier is demonstrated by simulation studies and real data examples.


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