Abstract
In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L 1 penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO (aLASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models.
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This research was supported by the National Natural Science Foundation of China under Grant No. 61272041.
This paper was recommended for publication by Editor SUN Liuquan.
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Lu, Y., Zhang, R. & Hu, B. The adaptive LASSO spline estimation of single-index model. J Syst Sci Complex 29, 1100–1111 (2016). https://doi.org/10.1007/s11424-015-4014-3
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DOI: https://doi.org/10.1007/s11424-015-4014-3