Abstract Details
Activity Number:
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41
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #307622 |
Title:
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Time-Varying Additive Models for Longitudinal Data
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Author(s):
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Xiaoke Zhang*+ and Byeong U. Park and Jane-Ling Wang
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Companies:
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University of California Davis and Seoul National University and UC Davis
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Keywords:
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Smooth backfitting ;
local linear smoothing ;
oracle property ;
functional data ;
longitudinal data
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Abstract:
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Additive model is an effective dimension reduction approach that also provides flexibility in modeling the relation between a response variable and key covariates. The literature is largely developed to scalar response and vector covariates. In this paper, more complex data is of interest, where both the response and covariates are functions. A functional additive model is proposed together with a new backfitting algorithm to estimate the unknown regression functions, whose components are time-dependent additive functions of the covariates. Such functional data may not be completely observed since measurements may only be collected intermittently at discrete time points. We develop a unified platform and an efficient approach that can cover both dense and sparse functional data and the needed theory for statistical inference. The oracle properties of the proposed estimators of the component functions are also established.
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Authors who are presenting talks have a * after their name.
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