主题：Sufficient dimension reduction in the presence of controlling variables
时间𓀋：2023年4月24号 10:00-11:30
地点： 腾讯会议：433-390-739
主持人：姜荣 教授
报告人简介🙎🏽♀️：
范国良，博士✍🏿，上海海事大学经济管理学院教授，应用统计硕士点负责人，美国《Mathematical Review》评论员，全国专业学位水平评估学位论文质量评价专家👨🦰🦹🏻♂️，中国现场统计研究会资源与环境统计分会理事，中国工程概率统计学会理事，中国商业统计学会理事。先后主持省部级以上项目10项🧝🏽♀️，其中国家级项目3项。在《Journal of Multivariate Analysis》《Statistica Sinica》《Electronic Journal of Statistics》《Journal of Statistical Planning and Inference》《Science China Mathematics》等国内外重要学术刊物上发表学术论文五十余篇。以第一完成人获省部级自然科学奖1项，上海市教学成果奖二等奖1项，上海海事大学教学成果奖特等奖1项。所授《计量经济学》课程获上海市高校一流本科课程🤵🏽♂️。
讲座简介：
In this talk, we are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables. We suggest a profile least squares approach to perform partial dimension reduction for a general class of semi-parametric models. The asymptotic properties of the resulting estimates for the central partial mean subspace and the mean function are provided. In addition, a Wald type test is proposed to evaluate a linear hypothesis of the central partial mean subspace, and a generalize likelihood ratio test is constructed to check whether the nonparametric mean function has a specific parametric form. These tests can be used to evaluate whether there exist interactions between the covariates and the controlling variables, and if any, in what form. A BIC-type criterion is applied to determine the structural dimension of the central partial mean subspace. Its consistency is also established. Numerical studies through simulations and real data examples are conducted to demonstrate the power and utility of the proposed semi-parametric approaches.
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