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Principal component models for sparse functional data. (English) Zbl 0962.62056
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.

MSC:
62H25 Factor analysis and principal components; correspondence analysis
62H12 Estimation in multivariate analysis
65C60 Computational problems in statistics (MSC2010)
Software:
fda (R)
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