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Activity Number: 485
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318964
Title: Weighing Schemes for Functional Data
Author(s): Xiaoke Zhang* and Jane-Ling Wang
Companies: University of Delaware and University of California at Davis
Keywords: Local linear smoothing ; Asymptotic normality ; L2 convergence ; Uniform convergence ; Weighing schemes

Nonparametric estimation of mean and covariance functions is important in functional data analysis. We investigate the performance of local linear smoothers for both mean and covariance functions with a general weighing scheme, which includes two commonly used schemes, equal weight per observation (OBS), and equal weight per subject (SUBJ), as two special cases. We provide a comprehensive analysis of their asymptotic properties on a unified platform for all types of sampling plan, be it dense, sparse, or neither. These two weighing schemes are compared both theoretically and numerically. We also propose a new class of weighing schemes in terms of a mixture of the OBS and SUBJ weights, of which theoretical and numerical performances are examined and compared.

Authors who are presenting talks have a * after their name.

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