Abstract :
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+
