Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables. For a high dimensional nonparametric nonlinear system, however, identifying whether a variable contributes or not is not easy. Therefore, based on the Fourier spectrum of density-weighted derivative, one novel variable selection approach is developed, which does not suffer from the dimensionality curse and improves the identification accuracy. Furthermore, a necessary and sufficient condition for testing a variable whether it contributes or not is provided. The proposed approach does not require strong assumptions on the distribution, such as elliptical distribution. The simulation study verifies the effectiveness of the novel variable selection algorithm.