  Linear regression and classification methods with repeated functional data
are considered. For each statistical unit in the sample, a real-valued
parameter is observed over time under different conditions. Two regression
methods based on fusion penalties are presented. The first one is a
generalization of the variable fusion methodology based on the 1-nearest
neighbor. The second one, called group fusion lasso, assumes some grouping
structure of conditions and allows for homogeneity among the regression
coefficient functions within groups. A finite sample numerical simulation and
an application on EEG data are presented.
