Model selection using a penalized data-splitting device is studied in the context of nonparametric regression. Finite sample bounds under mild conditions are obtained. The resulting estimates are adaptive for large classes of functions.
Keywords :
minimax hypothesis testing , nonparametric alternative , goodness-of-fit , Nonparametric regression , Fisher test , Fishers quantiles , Adaptive test , Model selection , linear hypothesis
