Suk, Hye Won; Hwang, Heungsun Functional generalized structured component analysis. (English) Zbl 1367.62321 Psychometrika 81, No. 4, 940-968 (2016). Summary: 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. MSC: 62P15 Applications of statistics to psychology 62H25 Factor analysis and principal components; correspondence analysis Keywords:generalized structured component analysis; functional data analysis; basis function expansion; splines; penalized least squares; alternating least squares Software:FITPACK; ElemStatLearn; PhysioToolkit; fda (R) PDFBibTeX XMLCite \textit{H. W. Suk} and \textit{H. Hwang}, Psychometrika 81, No. 4, 940--968 (2016; Zbl 1367.62321) Full Text: DOI Link References: [1] Abdi, H.; Lewis-Beck, M. (ed.); Bryman, A. (ed.); Futing, T. (ed.), Partial least squares (PLS) regression, 792-795 (2003), Thousand Oaks, CA [2] Byrd, R., Bilbert, J. C., & Nocedal, J. (2000). A Trust Region Method Based on Interior Point Techniques for Nonlinear Programming. Mathematical Programming A, 89, 149-185. · Zbl 1033.90152 [3] Byrd, R. H., Hribar, M. E., & Nocedal, J. (1999). 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