報(bào)告題目：A Semiparametric Approach to Dimension Reduction
主講人：馬彥源教授（南卡羅萊納大學(xué)）
時(shí)間：2015年7月3日15:00-16:00
地點(diǎn)：北院卓遠(yuǎn)樓305
主辦單位：統(tǒng)計(jì)與數(shù)學(xué)學(xué)院
摘要：
We provide a novel and completely different approach to dimension-reduction problems from the existing literature. We cast the dimension reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension reduction techniques as special cases in this class. The semiparametric approach also reveals that in the inverse regression context while keeping the estimation structure intact, the common assumption of linearity and/or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression. The semiparametric estimators without these common assumptions are illustrated through simulation studies and a real data example. This article has online supplementary material.
馬彥源教授簡(jiǎn)介：應(yīng)用數(shù)學(xué)博士（MIT），南卡羅萊納大學(xué)統(tǒng)計(jì)系教授,，主要研究領(lǐng)域?yàn)榘雲(yún)?shù)模型,，測(cè)量誤差模型，降維理論,，潛在變量模型等,。在Annals of Statistics, Journal of the Royal Statistical Society (Series B), Journal of the American Statistical Association, Biometrika等頂級(jí)統(tǒng)計(jì)學(xué)期刊發(fā)表論文數(shù)十篇，現(xiàn)擔(dān)任Journal of the Royal Statistical Society (Series B)的副主編,。
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