|
Activity Number:
|
229
|
|
Type:
|
Invited
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
IMS
|
| Abstract - #307905 |
|
Title:
|
Functional Regression Analysis: Models, Methods, and Applications
|
|
Author(s):
|
Hans-Georg Müller*+
|
|
Companies:
|
University of California, Davis
|
|
Address:
|
Department of Statistics, Davis, CA, 95616,
|
|
Keywords:
|
Functional Data Analysis ; Principal Component ; Smoothing ; Dimension Reduction
|
|
Abstract:
|
Functional regression analysis addresses the situation where predictors or responses in a regression setting include random functions. Early functional linear models were based on observing complete trajectories for the random functions. Generalized functional linear models can be used for classification. Recent extensions to be discussed include functional regression diagnostics, functional regression for sparse and noisy longitudinal data, time-varying functional models, functional additive and nonlinear regression, and the functional embedding of very high-dimensional data. A basic tool is the representation of random functions by functional principal component scores. Illustrations include data from longitudinal, demographic and gene expression studies. The presentation draws on joint work with Jeng-Min Chiou, Peter Hall, Xiaoyang Leng, Jane-Ling Wang, Ping-Shi Wu and Fang Yao.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2007 program |
