JSM 2011 Online Program

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Abstract Details

Activity Number: 127
Type: Contributed
Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #301925
Title: Functional Component Selection in Functional Additive Models
Author(s): Hongxiao Zhu*+ and Fang Yao and Hao "Helen" Zhang
Companies: Statistical and Applied Mathematical Sciences Institute and University of Toronto and North Carolina State University
Address: , , ,
Keywords: Component Selection ; Model Selection ; Additive Models ; Functional Data Analysis ; Smoothing Spline
Abstract:

Functional additive model (FAM) provides a flexible framework to model the relationship between the responses and functional predictors, and its additive structure naturally overcomes the curse of dimensionality. The problem of functional component selection in FAMs is an important issue but less studied in literature. In this work, we propose a new regularization framework for joint model estimation and functional component selection in the context of Reproducing Kernel Hilbert Space (RKHS). The proposed approach takes advantage of the uncorrelated structure of the functional PCA scores, which greatly facilitates the implementation of the approach. Asymptotic properties of the new estimator are studied, and extensive simulation studies are performed to assess the performance of the new method. We finally apply the method to a real data set.


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