Abstract Details
Activity Number:
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144
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309266 |
Title:
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Functional Generalized Model
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Author(s):
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Yichi Zhang*+ and Ana-Maria Staicu and Arnab Maity
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Companies:
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North Carolina State University and North Carolina State University and North Carolina State University
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Keywords:
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Functional Principal Component Analysis ;
Nonparametric Regression ;
Generalized Linear Model ;
Additive Model
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Abstract:
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We introduce a functional generalized model for semi-parametric modeling of the relation between a scalar response and functional covariates and discuss the estimation procedure. The methodology uses functional principal components analysis, which gives a parsimonious representation of the functional predictors as a linear combination of orthogonal eigenbasis functions and functional principal component scores, as well as greatly facilitates the estimation implementation. Our modeling approach assumes flexible non-parametric dependence between the response and the functional principal component scores, and allows for non-normal response. This modeling framework naturally extends the functional additive model, which restricts the dependence to an additive structure of non-linear functions of the scores. The proposed method is illustrated through simulations and real data application.
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Authors who are presenting talks have a * after their name.
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