報告題目：基于函數充分降維的函數線性模型估計
報 告 人：王國長副教授 暨南大學
報告時間：2020年7月4日 上午 11:30-12:30
報告地點：騰點擊鍊接入會，或添加至會議列表：
https://meeting.tencent.com/s/IjnNY3jilUiF
會議 ID：679 370 315
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
The functional linear regression model corresponding to a scalar response and a functional predictor is becoming increasingly common. Since the predictor is infinite dimensional, some form of dimension reduction is essential. There are two popular dimension reduction methods such as expanding the functional predictor or regression parameter function on the functional principal component basis or on a fixed bases (such as B-spline, Wavelet). In the present paper, we estimate the functional linear model by using the functional sufficient dimension reduction (FSDR) basis. Compared to the existing methods, the proposed method is appealing because the FSDR basis is related to both the functional predictor and the response variable, whereas the functional principal component basis is only related to the functional predictor and the fixed basis (B-spline, Wavelet) is independent from both the functional predictor and the response variable. Our techniques involve methods for giving a new expansion for the predictor, for giving a specific expression for the regression parameter function, for estimating the FSDR space by a new method and some asymptotical properties about the regression parameter function and the prediction for the test samples. Numerical studies, including both simulation studies and applications on real-life data, are presented to demonstrate the accuracy of the proposed method.
報告人簡介：
王國長，暨南大學經濟學院統計學系副教授，于2012年獲東北師範大學統計學博士學位，中科院數學與系統科學研究院從事博後工作2年。主要研究方向為：函數型數據分析、充分性降維、時間序列和機器學習等領域，至今已公開發表SCI論文近20餘篇；主持國家自然科學基金青年基金、博士後面上項目、廣東省自然科學基金面上項目各一項。
