This paper considers the feature screening method for the ultrahigh dimensional semiparametric linear models with longitudinal data. The C-statistic which measures the rank concordance between predictors and outcomes is generalized to the longitudinal data. On the basis of C-statistic and the score equation theory, we propose a feature screening method named LCSIS. Based on the smoothed technique and the score equations, the proposed estimating screening procedure is easy to compute and satisfies the feature screening consistency. Furthermore, Monte Carlo simulation studies and a real data application are conducted to examine the finite sample performance of the proposed procedure.

中文翻译：

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基于C统计量的超高维纵向数据特征筛选

本文研究了具有纵向数据的超高维半参数线性模型的特征筛选方法。衡量预测变量和结果之间等级一致性的 C 统计量被推广到纵向数据。基于C统计和得分方程理论，我们提出了一种名为LCSIS的特征筛选方法。基于平滑技术和得分方程，所提出的估计筛选过程易于计算并且满足特征筛选一致性。此外，还进行了蒙特卡罗模拟研究和实际数据应用，以检查所提出程序的有限样本性能。