title: Functional Generalized Structured Component Analysis.
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.
authors.affiliation: Department of Psychology, Arizona State University, 950 S. McAllister, BOX 871104, Tempe, AZ, 85287-1104 , USA. Hyewon.Suk@asu.edu.
authors.forename: Hye Won
authors.initials: HW
authors.lastname: Suk
authors.affiliation: McGill University, Montreal, QC, Canada.
authors.forename: Heungsun
authors.initials: H
authors.lastname: Hwang
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D001827:Body Height; D001835:Body Weight; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005684:Gait / Q000502:physiology; D006801:Humans; D017711:Nonlinear Dynamics; D010300:Parkinson Disease / Q000503:physiopathology; D011594:Psychometrics; D012720:Severity of Illness Index; D013223:Statistics as Topic
keywords: alternating least squares; basis function expansion; functional data analysis; generalized structured component analysis; penalized least squares; splines