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.
Original language | English (US) |
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Pages (from-to) | 524-542 |
Number of pages | 19 |
Journal | Psychometrika |
Volume | 77 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2012 |
Keywords
- alternating regularized least-squares algorithm
- extended redundancy analysis
- functional data
- penalized least squares
ASJC Scopus subject areas
- Psychology(all)
- Applied Mathematics