A Bayesian Model for Multivariate Functional Data
by Ori Rosen
Abstract: We propose a method for analyzing multivariate functional data with unequally spaced observation times that may differ among subjects.
Fitting multivariate observations simultaneously rather than fitting each variable separately may be advantageous if the error terms corresponding to each variable are correlated. Our method is formulated as a Bayesian mixed-effects model in which the fixed part corresponds to the mean functions, and the random part corresponds to individual deviations from these mean functions. Covariates can be incorporated into both the fixed and the random effects. The methodology is studied by simulation and illustrated with real data.
For More Information: contact: Owen Jones: email email@example.com