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2019概率统计及其应用系列报告之五(吴月华)

  发布日期:2019-04-24  浏览量:166


报告题目:Multiple change-points detection in generalized linear models

: 吴月华(加拿大约克大学教授,国际统计学会会员)

报告时间: 2019425(周四)  16:00-17:00

报告地点:磬苑校区澳门赌搏网站大全H306

报告摘要:In this talk, we focus on the problem of multiple change points estimation in GLMs in which both number of change points and their locations are unknown. We propose a simultaneous multiple change points estimation method which first partitions the data sequence into several segments to construct a new design matrix, secondly convert the multiple change points estimation problem into a variable selection problem, and then estimate the regression coefficients by maximizing a penalized likelihood function. The consistency of the coefficient estimator is established in which the number of coefficients can diverge as the sample size goes to infinity. The nonzero coefficient estimates provide the information about which segments potentially contain a change point. An algorithm is provided to estimate the multiple change points. Simulation studies are conducted for both logistic and log-linear models. A real data application is also presented. Joint work with X. Sun

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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。吴教授研究领域包括空间统计、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、生物医学等交叉学科。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,发表学术论文百余篇,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 AmericaPNAS美国国家科学院院刊)论文。

 

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