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Functional Generalized Structured Component Analysis

Published online by Cambridge University Press:  01 January 2025

Hye Won Suk  and
Heungsun Hwang
Hye Won Suk*
Affiliation:
Arizona State University
Heungsun Hwang
Affiliation:
McGill University
*
Correspondence should be made to Hye Won Suk, Department of Psychology, Arizona State University, 950 S. McAllister, BOX 871104, Tempe, AZ 85287-1104 USA. Email: Hyewon.Suk@asu.edu
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Abstract

An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

Keywords

generalized structured component analysisfunctional data analysisbasis function expansionsplinespenalized least squaresalternating least squares

Information

Type
Article
Information
Psychometrika , Volume 81 , Issue 4 , December 2016 , pp. 940 - 968
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (doi:10.1007/s11336-016-9521-1) contains supplementary material, which is available to authorized users.

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