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SUMMARY:Functional regression on manifold with contamination - Fang Yao (U
 niversity of Toronto\; University of Toronto)
DTSTART:20180320T160000Z
DTEND:20180320T170000Z
UID:TALK102679@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:We propose a new perspective on functional regression with a p
 redictor process via the concept of manifold that is intrinsically finite-
 dimensional and embedded in an infinite-dimensional functional space\, whe
 re the predictor is contaminated with discrete/noisy measurements.  By a n
 ovel method of  functional local linear manifold smoothing\, we achieve a 
 polynomial rate of convergence that adapts to the intrinsic manifold dimen
 sion and the level of sampling/noise contamination with a phase transition
  phenomenon depending on their interplay. This is in contrast to the logar
 ithmic convergence rate in the literature of functional nonparametric regr
 ession. We demonstrate that the proposed method enjoys favorable finite sa
 mple performance relative to commonly used methods via simulated and real 
 data examples. (Joint with Zhenhua Lin)  
LOCATION:Seminar Room 1\, Newton Institute
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