title: Functional Extended Redundancy Analysis.
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.
authors: Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada. heungsun.hwang@mcgill.ca.
authors: Heungsun
authors: H
authors: Hwang
authors: Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada.
authors: Hye Won
authors: HW
authors: Suk
authors: Chung-Ang University, Seoul, Korea.
authors: Jang-Han
authors: JH
authors: Lee
authors: Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada.
authors: D S
authors: DS
authors: Moskowitz
authors: Concordia University, Montreal, Canada.
authors: Jooseop
authors: J
authors: Lim
keywords: alternating regularized least-squares algorithm; extended redundancy analysis; functional data; penalized least squares