B0803
Title: Regression models with repeated functional data
Authors: Issam-Ali Moindjie - University of Quebec in Montreal (Canada) [presenting]
Cristian Preda - University of Lille (France)
Abstract: Linear regression and classification models with repeated functional data are considered. A real-valued parameter is observed over time under different conditions for each statistical unit in the sample. Two regression models based on fusion penalties are presented. The first is a generalization of the variable fusion model based on the 1-nearest neighbour. 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 to EEG data are presented.
