The Adaptive LASSO Spline Estimation of Single-Index Model  

作　　者：LU Yiqiang ZHANG Riquan HU Bin 

机构地区：[1]The PLA Information Engineering University,Zhengzhou 450004,China [2]School of Finance and Statistics,East China Normal University,Shanghai 200241,China

出　　处：《Journal of Systems Science & Complexity》2016年第4期1100-1111,共12页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.61272041

摘　　要：In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 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(aL ASSO) 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.

关 键 词：Adaptive LASSO B-SPLINE oracle property single-index model variable selection. 

分 类 号：O212.1[理学—概率论与数理统计]