LOCALIZED MODEL SELECTION FOR REGRESSION

Research on model0procedure selection has focused on selecting a single model globally+ In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be improved when selection of a model is allowed to depend on location+ We consider localized model selection methods and derive their theoretical properties+