|Title||Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Storlie, Curtis B., Howard D. Bondell, Brian J. Reich, and Hao Helen Zhang|
|Date Published||2011 Apr|
Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.
|Alternate Journal||Stat Sin|
|Original Publication||Surface estimation, variable selection, and the nonparametric oracle property.|
|PubMed Central ID||PMC3095957|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA085848-10 / CA / NCI NIH HHS / United States
Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.