Longitudinal Functional Principal Component Analysis
We introduce models for the analysis of functional data observed at multiple time points. The model can be viewed as the functional analog of the classical mixed effects model where random effects are replaced by random processes. Computational feasibility is assured by using principal component bases. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study on multiple sclerosis.
KeywordsPrincipal Component Analysis Multiple Sclerosis Random Effect Pattern Recognition Stochastic Process
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