讲座主题：A generalized semiparametric regression and its efficient estimation
主讲人：林路
讲座时间：2021.4.23（周五）  10：00-11：00
地点：B131
主讲人简介：  

      林路是山东大学金融研究院教授、博士生导师；在南开大学获得博士学位后，先在南开大学任教，然后到山东大学任教至今；从事大数据、高维统计、非参数和半参数统计以及金融统计等方的研究，在国内外统计学、机器学习和数学顶级期刊(包括Annals of Statistics, Journal of Machine Learning Research, 中国科学)和其它重要期刊发表研究论文120余篇；主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等；获得山东省优秀教学成果一等奖(排名第一)；是教育部应用统计专业硕士教育指导委员会成员，山东省政府参事。

讲座摘要：

We investigate a generalized semiparametric regression. Such a model can avoid the risk of wrongly choosing the base measure function. We propose a profile likelihood to efficiently estimate both parameter and nonparametric function. The main difference from the classical profile likelihood is that the profile likelihood proposed is a functional of the base measure function, instead of a function of a real variable. By making the most of the structure information of the semiparametric exponential family, we get an explicit expression of the estimator of the least favorable curve. It ensures that the new profile likelihood is computationally simple. Due to the use of the least favorable curve, the semiparametric efficiency is achieved successfully and the estimation bias is reduced significantly. Simulation studies can illustrate that our proposal is much better than the existing methodologies for most cases under study, and is robust to the different model conditions.