纵向数据下线性EV模型的变量选择(英文)  

Variable Selection for the Linear EV Model with Longitudinal Data

作　　者：田瑞琴[1] 薛留根[1] 

机构地区：[1]北京工业大学应用数理学院,北京100124

出　　处：《应用概率统计》2013年第3期246-260,共15页Chinese Journal of Applied Probability and Statistics

基　　金：The project supported by the National Natural Science Foundation of China(11171012);the Science and Technology Project of the Faculty Adviser of Excellent PHD Degree Thesis of Beijing(20111000503);the Beijing Municipal Education Commission Foundation(KM201110005029);Beijing Municipal Key Disciplines(006000541212010)

摘　　要：本文考虑了纵向数据线性EV模型的变量选择.基于二次推断函数方法和压缩方法的思想提出了一种新的偏差校正的变量选择方法.在选择适当的调整参数下,我们证明了所得到的估计量的相合性和渐近正态性.最后通过模拟研究验证了所提出的变量选择方法的有限样本性质.In this paper, we focus on the variable selection for the linear EV model with longitudinal data when some covariates are measured with errors. A new bias-corrected variable selection procedure is proposed based on the combination of the quadratic inference functions and shrinkage estimations. With appropriate selection of the tuning parameters, we establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedures.

关 键 词：线性EV模型 变量选择 纵向数据 二次推断函数 

分 类 号：O212[理学—概率论与数理统计]