纵向数据下线性EV模型的变量选择

Variable Selection for the Linear EV Model with Longitudinal Data

  • 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.

     

    Abstract: 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.

     

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