This paper considers a partially linear regression model relating a right-censored response variable to predictors and an extra covariate with measured error. The main problem here is that censorship and measurement error problems need to be solved to estimate the model correctly. In this sense, we propose three modified semiparametric estimators obtained from local polynomial regression, kernel smoothing, and B-spline smoothing methods based on kernel deconvolution approach and synthetic data transformation. Here, kernel deconvolution technique is used to solve the measurement error problem in the model and synthetic data transformation is considered to add the effect of censorship to the estimation procedure, which is a very common method in the literature. The performances of the introduced estimators are compared in the detailed Monte-Carlo simulation study. In addition, Carotid endarterectomy data is used as real-world data example and results are presented. According to the results, it is seen that the deconvoluted local polynomial method gives more qualified estimates than other two methods.

中文翻译：

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具有变量误差的右删失部分线性回归模型：在颈动脉内膜切除术数据集上的应用

本文考虑了一个部分线性回归模型，该模型将右截尾响应变量与预测变量和具有测量误差的额外协变量相关联。这里的主要问题是需要解决审查和测量误差问题才能正确估计模型。从这个意义上讲，我们提出了三种改进的半参数估计量，这些估计量是从局部多项式回归、核平滑和基于核反卷积方法和合成数据变换的 B 样条平滑方法获得的。这里，核反卷积技术用于解决模型中的测量误差问题，并考虑合成数据变换以在估计过程中添加检查效果，这是文献中非常常见的方法。在详细的蒙特卡洛模拟研究中比较了引入的估计器的性能。此外，颈动脉内膜切除术数据被用作真实世界的数据示例并给出了结果。根据结果​​可以看出，反卷积局部多项式方法比其他两种方法给出了更合格的估计。