一、主講人介紹：       

王躍東博士，美國(guó)加州大學(xué)圣巴巴拉分校終身教授，是統(tǒng)計(jì)學(xué)界具有卓越貢獻(xiàn)的研究者，為國(guó)際統(tǒng)計(jì)學(xué)院當(dāng)選會(huì)士、美國(guó)統(tǒng)計(jì)學(xué)會(huì)當(dāng)選會(huì)士、英國(guó)皇家學(xué)會(huì)會(huì)士，是國(guó)際數(shù)理統(tǒng)計(jì)協(xié)會(huì)、泛華統(tǒng)計(jì)協(xié)會(huì)、國(guó)際統(tǒng)計(jì)科學(xué)學(xué)會(huì)的會(huì)員。致力于統(tǒng)計(jì)學(xué)方法及其應(yīng)用的研究，圍繞平滑樣條、混合效應(yīng)模型、生存分析、縱向數(shù)據(jù)、微陣列數(shù)據(jù)分析等方向，在統(tǒng)計(jì)學(xué)國(guó)際頂尖學(xué)術(shù)期刊（Journal of the American Statistical Association、Annals of Statistics、Journal of the Royal Statistical Society、Biometrika 等）發(fā)表高水平論文三十余篇。

二、講座信息

Estimation and model selection for nonparametric function-on-function regression：Regression models with functional response and functional covariates have recently received significant attention. While various nonparametric and semiparametric models have been developed, there is an urgent need for model selection and diagnostic methods. This study present a unified framework for estimation and model selection in nonparametric function-on-function regression. We consider a general nonparametric functional regression model with the model space constructed through smoothing spline analysis of variance (SS ANOVA). The proposed model reduces to some existing models when selected components in the SS ANOVA decomposition are eliminated. We propose new estimation procedures under either L1 or L2 penalty and show that combining the SS ANOVA decomposition and the L1 penalty provides powerful tools for model selection and diagnostics. We establish consistency and convergence rates for estimates of the regression function and each component in its decomposition under both the L1 and L2 penalties. Simulation studies and real examples show that the proposed methods perform well. 

講座時(shí)間：2023年7月10日（星期二） 9:00-10:30

講座地點(diǎn)：經(jīng)濟(jì)學(xué)院  經(jīng)濟(jì)3室

歡迎大家積極參加！

三、主辦單位

經(jīng)濟(jì)學(xué)院

國(guó)際合作與交流處

2023年7月3日