Functional Generalized Structured Component Analysis

Hye Won Suk () and Heungsun Hwang
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Hye Won Suk: Arizona State University
Heungsun Hwang: McGill University

Psychometrika, 2016, vol. 81, issue 4, No 3, 940-968

Abstract: 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 analysis; functional data analysis; basis function expansion; splines; penalized least squares; alternating least squares (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11336-016-9521-1

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