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Functional Extended Redundancy Analysis

Published online by Cambridge University Press:  01 January 2025

Heungsun Hwang*
Affiliation:
McGill University
Hye Won Suk
Affiliation:
McGill University
Jang-Han Lee
Affiliation:
Chung-Ang University
D. S. Moskowitz
Affiliation:
McGill University
Jooseop Lim
Affiliation:
Concordia University
*
Requests for reprints should be sent to Heungsun Hwang, Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada. E-mail: heungsun.hwang@mcgill.ca

Abstract

We propose a functional version of extended redundancy analysis that examines directional relationships among several sets of multivariate variables. As in extended redundancy analysis, the proposed method posits that a weighed composite of each set of exogenous variables influences a set of endogenous variables. It further considers endogenous and/or exogenous variables functional, varying over time, space, or other continua. Computationally, the method reduces to minimizing a penalized least-squares criterion through the adoption of a basis function expansion approach to approximating functions. We develop an alternating regularized least-squares algorithm to minimize this criterion. We apply the proposed method to real datasets to illustrate the empirical feasibility of the proposed method.

Information

Type
Original Paper
Copyright
Copyright © 2012 The Psychometric Society

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