Conference proceeding
Consistent variable selection for high-dimensional nonparametric additive nonlinear systems
2016 IEEE 55th Conference on Decision and Control (CDC), pp.3066-3071
12/2016
DOI: 10.1109/CDC.2016.7798728
Abstract
In this paper, the problem of variable selection is addressed for high-dimensional nonparametric additive nonlinear systems. The purpose of variable selection is to determine contributing additive functions and to remove non-contributing ones from the underlying nonlinear system. A two-step method is developed to conduct variable selection. The first step is concerned with estimating each additive function by virtue of kernel-based nonparametric approaches. The second step is to apply a nonnegative garrote estimator to identify which additive functions are nonzero in terms of the obtained non-parametric estimates of each function. The proposed variable selection method is workable without suffering from the curse of dimensionality, and it is able to find the correct variables with probability one under weak conditions as the sample size approaches infinity. The good performance of the proposed variable selection method is demonstrated by a numerical example.
Details
- Title: Subtitle
- Consistent variable selection for high-dimensional nonparametric additive nonlinear systems
- Creators
- Biqiang Mu - Chinese Academy of SciencesWei Xing Zheng - The University of SydneyEr-Wei Bai - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2016 IEEE 55th Conference on Decision and Control (CDC), pp.3066-3071
- DOI
- 10.1109/CDC.2016.7798728
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2016
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197169802771
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