James, Gareth M.; Hastie, Trevor J.; Sugar, Catherine A. Principal component models for sparse functional data. (English) Zbl 0962.62056 Biometrika 87, No. 3, 587-602 (2000). Summary: The elements of a multivariate dataset are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete measurements for each individual curve or, as is more common, one has measurements on a fine grid taken at the same time points for all curves, then many standard techniques may be applied. However, curves are often measured at an irregular and sparse set of time points which can differ widely across individuals. We present a technique for handling this more difficult case using a reduced rank mixed effects framework. Cited in 140 Documents MSC: 62H25 Factor analysis and principal components; correspondence analysis 62H12 Estimation in multivariate analysis 65C60 Computational problems in statistics (MSC2010) Keywords:functional data analysis; growth curve; mixed effects model; reduced rank estimation; principal components Software:fda (R) PDFBibTeX XMLCite \textit{G. M. James} et al., Biometrika 87, No. 3, 587--602 (2000; Zbl 0962.62056) Full Text: DOI Link