北京师范大学统计学院
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Seminar&Conferences
Functional Regression on Manifold with Contamination(No.8 2017)

Report:Functional Regression on Manifold with Contamination

Time: 15:00pm  Nov.2,2017

Place:Room104,Building of Statistics of School

Reporter:Yao Fang

Abstract:We propose a new perspective on functional regression with a predictor process via the concept of manifold that is intrinsically finite-dimensional and embedded in an infinite-dimensional functional space, where the predictor is contaminated with discrete/noisy measurements.  By a novel method of  functional local linear manifold smoothing, we achieve a polynomial rate of convergence that adapts to the intrinsic manifold dimension and the level of sampling/noise contamination with a phase transition phenomenon depending on their interplay. This is in contrast to the logarithmic convergence rate in the literature of functional nonparametric regression. We demonstrate that the proposed method enjoys favourable finite sample performance relative to commonly used methods via simulated and real data examples.




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