報告題目：Functional Regression on Manifold with Contamination
報 告 人：姚方教授 北京大學
報告時間：2020年6月17日 15:00-16:00
報告地點：騰訊會議 ID：442 184 257
會議密碼：0617
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校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：
We propose a new method for functional nonparametric regression with a predictor that resides on a finite-dimensional manifold but is only observable in an infinite-dimensional space. Contamination of the predictor due to discrete/noisy measurements is also accounted for. By using functional local linear manifold smoothing, the proposed estimator enjoys a polynomial rate of convergence that adapts to the intrinsic manifold dimension and the contamination level. This is in contrast to the logarithmic convergence rate in the literature of functional nonparametric regression. We also observe a phase transition phenomenon regarding the interplay of the manifold dimension and the contamination level. We demonstrate that the proposed method has favorable numerical performance relative to commonly used methods via simulated and real data examples.
報告人簡介：
姚方，北京大學博雅講席教授, 北大統計科學中心主任，數理統計學會（IMS）Fellow，美國統計學會（ASA）Fellow。2000年本科畢業于中國科技大學統計專業，2003獲得加利福尼亞大學戴維斯分校統計學博士學位，曾任職于多倫多大學統計科學系終身教授。現擔任Canadian Journal of Statistics的主編，至今擔任9個國際統計學核心期刊編委，包括統計學頂級期刊Journal of the American Statistical Association和 Annals of Statistics。
