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        山東大學林路教授學術報告
        發布日期:2018-10-23 瀏覽次數:

        報告題目Semiparametric Efficient Estimation for Semiparametric Exponential Family via Profile Likelihood

        報告人:林路  教授

        報告摘要: 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.

        報告時間10月30日上午10:10-11:10

        報告地點:統計學院211教室

        報告人簡介:林路是山東大學金融研究院教授、博士生導師、副院長;在南開大學獲得博士學位后,先在南開大學任教,然后到山東大學任教至今;從事高維統計、非參數和半參數統計以及金融統計等方的研究,在國際統計學、機器學習和相關應用學科頂級期刊Annals of Statistics, Journal of Machine Learning Research, PLoS computational biology和其它重要期刊發表研究論文90余篇;主持過多項國家自然科學基金課題、博士點專項基金課題、山東省自然科學基金重點項目等;獲得國家統計局頒發的統計科技進步一、二等獎,山東省優秀教學成果一等獎;是國家973項目、國家創新群體和教育部創新團隊的核心成員,教育部應用統計專業碩士教育指導委員會成員,山東省政府參事。


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