Linear regression and classification models 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 models based on fusion penalties are presented. The first one is a generalization of the variable fusion model 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.

翻译：本文考虑具有重复函数数据的线性回归和分类模型。对于样本中的每个统计单元，在不同条件下随时间观测到一个实值参数。提出了两种基于融合惩罚的回归模型。第一种是对基于1-最近邻的变量融合模型的推广。第二种称为组融合套索（group fusion lasso），假设条件存在某种分组结构，并允许组内回归系数函数具有同质性。文中给出了有限样本数值模拟以及在脑电图数据上的应用实例。