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一、主講人介紹：       

王躍東博士，美國加州大學圣巴巴拉分校終身教授，是統計學界具有卓越貢獻的研究者，為國際統計學院當選會士、美國統計學會當選會士、英國皇家學會會士，是國際數理統計協會、泛華統計協會、國際統計科學學會的會員。致力于統計學方法及其應用的研究，圍繞平滑樣條、混合效應模型、生存分析、縱向數據、微陣列數據分析等方向，在統計學國際頂尖學術期刊（Journal of the American Statistical Association、Annals of Statistics、Journal of the Royal Statistical Society、Biometrika 等）發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. 

講座時間：2023年7月10日（星期二） 9:00-10:30

講座地點：經濟學院  經濟3室

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三、主辦單位

經濟學院

國際合作與交流處

2023年7月3日