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2019概率统计及其应用系列报告之一(吴月华)

  发布日期:2019-02-22  浏览量:299


报告题目:Regression clustering and its application

: 吴月华(加拿大约克大学教授国际统计学会会员)

报告时间: 2019225(周一)  16:30-17:30

报告地点:磬苑校区澳门赌搏网站大全H306

报告摘要:Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point and estimating the regression coefficients of the model. In this talk, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. We also present simulation studies for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. We end the talk by briefly discussing orthogonal regression clustering. Joint work with G. Qian and others.

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专家概况:吴月华,加拿大约克大学(York University)数学与统计系教授;1989年获得美国匹兹堡大学统计学博士学位,师从世界著名统计学家C.R. Rao。吴教授研究兴趣包括空间统计、高维统计、变点检验、模型选择非参数估计、金融统计以及在环境科学、信息科学、生物医学等领域中的应用。目前当选国际统计学会的会员(Elected member of International Statistical Institute),承担了一些加拿大重要科研项目,发表学术论文篇,其中包括5篇国际最顶级期刊Proceedings of the National Academy of Sciences of the United States of America(美国国家科学院院刊)论文


 

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