Abstract
It is known that feature selection/screening for high-dimensional nonparametric models is an important but very difficult issue. In this paper, we first point out the limitations of existing screening methods. In particular, model-free sure independence screening methods, which are defined on random predictors, may completely miss some important features in the underlying nonparametric function when the predictors follow certain distributions. To overcome these limitations, we propose an ensemble screening procedure for nonparametric models. It elaborately combines several existing screening methods and outputs a result close to the best one of these methods. Numerical examples indicate that the proposed method is very competitive and has satisfactory performance even when existing methods fail.
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