Modeling Longitudinal Data on Riemannian Manifolds

12 Dec 2018Xiongtao DaiZhenhua LinHans-Georg Müller

When considering functional principal component analysis for sparsely observed longitudinal data that take values on a nonlinear manifold, a major challenge is how to handle the sparse and irregular observations that are commonly encountered in longitudinal studies. Addressing this challenge, we provide theory and implementations for a manifold version of the principal analysis by conditional expectation (PACE) procedure that produces representations intrinsic to the manifold, extending a well-established version of functional principal component analysis targeting sparsely sampled longitudinal data in linear spaces... (read more)

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