We study the problem of multivariate regression where the data are naturally grouped, and a regression matrix is to be estimated for each group. We propose an approach in which a dictionary of low rank parameter matrices is estimated across groups, and a sparse linear combination of the dictionary elements is estimated to form a model within each group. We refer to the method as conditional spa...

This study investigates the minimaxity of a multi-task nonparametric regression problem. We formulate simultaneous function estimation problem based on information pooling across multiple experiments under low-dimensional structure. A reduced rank estimator nuclear norm penalisation scheme is proposed to incorporate structure in process. set functions defined terms their Fourier coefficients fo...

We study the problem of multivariate regression where the data are naturally grouped, and a regression matrix is to be estimated for each group. We propose an approach in which a dictionary of low rank parameter matrices is estimated across groups, and a sparse linear combination of the dictionary elements is estimated to form a model within each group. We refer to the method as conditional spa...