【12月27日】统计学学术讲座
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作者：
发布时间：2019-12-02
报告题目：Statistical Learning of the Worst Regional Smog Extremes with Dynamic Conditional Modeling
主讲人：张正军教授（美国威斯康辛大学）
时间：2019年12月27日（周五）14:00 p.m.
地点：北院卓远楼305会议室
主办单位：统计与数学学院
摘要：This talk is concerned with the statistical learning of extreme smog (PM2.5) dynamics of a vast region in China. Using classical extreme value theory, one can fit the generalized extreme value distribution to extreme observations recorded from each of those hundreds of smog monitoring stations. The proposed work intends to integrate classical extreme value modeling and dynamic modeling into a dynamic conditional distribution modeling and analysis of regional smog extremes, in particular, worst scenarios observed at one or multiple locations in each day. In addition, weather factors will be introduced in the model to gain higher modeling efficiency. The proposed model and the enhanced model will be illustrated with real data of hourly PM2.5 observations between 2014-2016 from smog monitoring stations located in the Beijing-Tianjin-Hebei geographical region. The results show a significant improvement compared with using a static extreme value analysis alone. The findings enhance the understanding of how severe extreme smog scenarios can be and provide useful information for the central/local government to conduct coordinated PM2.5 control and treatment. For completeness, probabilistic properties of the proposed model are investigated. Statistical estimation based on conditional maximum likelihood principle is established. To demonstrate the estimation and inference efficiency of studies, extensive simulations are also implemented. Based on a joint work with Mengxin Yu and Lu Deng.
主讲人简介：
张正军教授，现为美国威斯康辛大学统计系长聘正教授、美国统计协会会士、国际数理统计协会财务总监、国际顶级期刊“商业和经济统计”副主编、“计量经济学期刊”金融工程与风险管理特刊共同主编、“泛华统计学报Statistica Sinica”副主编。张正军教授2002年毕业于北卡罗来纳大学教堂山分校，获统计学博士学位。主要研究方向包括：金融时间序列分析、极值理论、异常气候分析、稀有疾病（癌症、帕金森综合症、奥兹海默病等等）分析、金融风险的建模和评估、市场系统性风险评估等等。
主讲人：张正军教授（美国威斯康辛大学）
时间：2019年12月27日（周五）14:00 p.m.
地点：北院卓远楼305会议室
主办单位：统计与数学学院
摘要：This talk is concerned with the statistical learning of extreme smog (PM2.5) dynamics of a vast region in China. Using classical extreme value theory, one can fit the generalized extreme value distribution to extreme observations recorded from each of those hundreds of smog monitoring stations. The proposed work intends to integrate classical extreme value modeling and dynamic modeling into a dynamic conditional distribution modeling and analysis of regional smog extremes, in particular, worst scenarios observed at one or multiple locations in each day. In addition, weather factors will be introduced in the model to gain higher modeling efficiency. The proposed model and the enhanced model will be illustrated with real data of hourly PM2.5 observations between 2014-2016 from smog monitoring stations located in the Beijing-Tianjin-Hebei geographical region. The results show a significant improvement compared with using a static extreme value analysis alone. The findings enhance the understanding of how severe extreme smog scenarios can be and provide useful information for the central/local government to conduct coordinated PM2.5 control and treatment. For completeness, probabilistic properties of the proposed model are investigated. Statistical estimation based on conditional maximum likelihood principle is established. To demonstrate the estimation and inference efficiency of studies, extensive simulations are also implemented. Based on a joint work with Mengxin Yu and Lu Deng.
主讲人简介：
张正军教授，现为美国威斯康辛大学统计系长聘正教授、美国统计协会会士、国际数理统计协会财务总监、国际顶级期刊“商业和经济统计”副主编、“计量经济学期刊”金融工程与风险管理特刊共同主编、“泛华统计学报Statistica Sinica”副主编。张正军教授2002年毕业于北卡罗来纳大学教堂山分校，获统计学博士学位。主要研究方向包括：金融时间序列分析、极值理论、异常气候分析、稀有疾病（癌症、帕金森综合症、奥兹海默病等等）分析、金融风险的建模和评估、市场系统性风险评估等等。
