VARIABLE SELECTION FOR COVARIATE ADJUSTED REGRESSION MODEL

This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the...

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Bibliographic Details
Published in:Journal of systems science and complexity 2014-12, Vol.27 (6), p.1227-1246
Main Authors: Li, Xuejing, Du, Jiang, Li, Gaorong, Fan, Mingzhi
Format: Article
Language:English
Subjects:
BIC准则
Complex Systems
Control
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Statistics
Systems Theory
协变量
变量选择
最小二乘估计
最小二乘法
未知函数
线性回归模型
调整
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Summary:This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called ＂oracle property＂. In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-014-2276-9