Functional Extended Redundancy Analysis

Heungsun Hwang, Hye Won Suk, Jang Han Lee, D. S. Moskowitz, Jooseop Lim

Research output: Contribution to journalArticle

7 Scopus citations

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 languageEnglish (US)
Pages (from-to)524-542
Number of pages19
JournalPsychometrika
Volume77
Issue number3
DOIs
StatePublished - Jul 1 2012

Keywords

  • alternating regularized least-squares algorithm
  • extended redundancy analysis
  • functional data
  • penalized least squares

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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    Hwang, H., Suk, H. W., Lee, J. H., Moskowitz, D. S., & Lim, J. (2012). Functional Extended Redundancy Analysis. Psychometrika, 77(3), 524-542. https://doi.org/10.1007/s11336-012-9268-2