This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation into consideration and is more efficient and robust than some parametric model assumptions for the working covariance matrix in each subject or experimental unit. We also show that the proposed method enjoys the sure screening property under some regularity conditions. Monte Carlo simulation studies and a real data application are conducted to examine the performance of the proposed method.

中文翻译：

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超高维纵向数据二次推理函数的特征筛选

本文关注的是具有纵向数据的超高维可加模型的特征筛选。所提出的方法利用二次推理函数来构建边缘筛选测量，它考虑了被试内的相关性，并且比每个被试或实验单元的工作协方差矩阵的一些参数模型假设更有效和稳健。我们还表明，所提出的方法在某些规律性条件下具有确定的筛选特性。进行蒙特卡罗模拟研究和实际数据应用以检查所提出方法的性能。