Title
Component Selection and Smoothing in High Dimensional Nonparametric Regression
Speaker
Yi Lin, University of Wisconsin
Abstract
We propose a new method for model selection and model fitting in nonparametric regression models, in the framework of smoothing spline ANOVA. The ``COSSO'' is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properties, such as the existence and the rate of convergence of the COSSO estimator, are studied. In the special case of a tensor product design with periodic functions, a detailed analysis reveals that the COSSO does model selection by applying a novel soft thresholding type operation to the function components. We give an equivalent formulation of the COSSO estimator which leads naturally to an iterative algorithm. We compare the COSSO with the MARS, a popular method that builds functional ANOVA models, in simulations and real examples. The COSSO gives very competitive performances in these studies.
