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        山东大学林路教授学术报告
        发布日期:2018-10-23 浏览次数:

        报告题目Semiparametric Efficient Estimation for Semiparametric Exponential Family via Profile Likelihood

        报告人:林路  教授

        报告摘要: Semiparametric exponential family is an extension of the parametric exponential family to the case with a nonparametric base measure function. Such a distribution family has potential application in the cases of incomplete data, selection bias, heterogeneity and so on. However, the methodology for achieving the semiparametric efficiency has not been proposed in the existing literature. In this paper, we propose a profile likelihood to efficiently estimate both parameter and nonparametric function. Due to the use of the least favorable curve in the procedure of profile likelihood, the semiparametric efficiency is achieved successfully and the estimation bias is reduced significantly. Moreover, by making the most of the structure information of the semiparametric exponential family, the estimator of the least favorable curve has an explicit expression. It ensures that the newly proposed profile likelihood can be implemented and is computationally simple. Simulation studies can illustrate that our proposal is much better than the existing methodology for most cases under study, and is robust to the different model conditions.

        报告时间10月30日上午10:10-11:10

        报告地点?#21644;?#35745;学院211教室

        报告人简介:林路是山东大学金融研究院教授、博?#21487;?#23548;师、副院长;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;从事高维统计、非参数和半参数统计以及金融统计?#30830;?#30340;研究,在国际统计学、机器学习和相关应用学科顶级期刊Annals of Statistics, Journal of Machine Learning Research, PLoS computational biology和其它重要期刊发表研究论文90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;获得国家统计?#32844;?#21457;的统计科技进步一、二等奖,山东省优秀教学成果一等奖;是国家973项目、国家创新群体和教育部创新团队的核心成员,教育部应用统计专业硕士教育指导委?#34987;?#25104;员,山东省政府参事。


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