Functional canonical analysis for square integrable stochastic processes

Authors:
Guozhong He;Hans-Georg Müller;Jane-Ling Wang
Affiliations:
The California Diabetes Control Program, University of California, San Francisco/DHS, 601 North 7th Street, MS 675, Sacramento, CA;Department of Statistics, University of California, One Shields Avenue, Davis, CA;Department of Statistics, University of California, One Shields Avenue, Davis, CA
Venue:
Journal of Multivariate Analysis
Year:
2003

Citing 2
Cited 5

Time series: data analysis and theory

Time series: data analysis and theory
Functional Data Analysis with R and MATLAB

Functional Data Analysis with R and MATLAB

Penalized solutions to functional regression problems

Computational Statistics & Data Analysis
Statistical Consistency of Kernel Canonical Correlation Analysis

The Journal of Machine Learning Research
Some properties of canonical correlations and variates in infinite dimensions

Journal of Multivariate Analysis
An extension of Fisher's discriminant analysis for stochastic processes

Journal of Multivariate Analysis
Extreme learning machines for soybean classification in remote sensing hyperspectral images

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study the extension of canonical correlation from pairs of random vectors to the case where a data sample consists of pairs of square integrable stochastic processes. Basic questions concerning the definition and existence of functional canonical correlation are addressed and sufficient criteria for the existence of functional canonical correlation are presented. Various properties of functional canonical analysis are discussed. We consider a canonical decomposition, in which the original processes are approximated by means of their canonical components.