In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously. With some regularization conditions, the resulting estimators of parameters are asymptotically normal and the estimators of nonparametric varying coefficient achieves the optimal convergence rate. Furthermore, we present simulation studies and a real example analysis to evaluate the finite sample performance of the proposed method.

在本文中，我们考虑了当协变量以一些加性误差测量时，纵向部分线性可变系数变量误差 (EV) 模型的估计和模型选择。在具有两个罚函数项的二次推理函数的基础上，提出了修正偏差的惩罚二次推理函数方法。所提出的方法不仅可以处理协变量和主体内相关性的测量误差，而且可以同时估计和选择重要的非零参数和非参数分量。在一定的正则化条件下，得到的参数估计量是渐近正态的，非参数变系数的估计量达到最优收敛速度。此外，