We propose a novel inference procedure for linear combinations of high-dimensional regression coefficients in generalized estimating equations, which have been widely used for correlated data analysis for decades. Our estimator, obtained via constructing a system of projected estimating equations, is shown to be asymptotically normally distributed under certain regularity conditions. We also introduce a data-driven cross-validation procedure to select the tuning parameter for estimating the projection direction, which is not addressed in the existing procedures. We demonstrate the robust finite-sample performance, especially in estimation bias and confidence interval coverage, of the proposed method via extensive simulations, and apply the method to gene expression data on riboflavin production with Bacillus subtilis.

翻译：我们为通用估计方程式中高维回归系数的线性组合提出了一个新的推论程序,数十年来,通用估计方程式已广泛用于相关数据分析。我们通过建立预测估计方程式系统获得的测算器显示,在某些正常情况下,其正常分布是零星的。我们还采用了数据驱动的交叉校验程序,以选择用于估计预测方向的调控参数,而现有程序没有涉及这一点。我们通过广泛模拟,展示了拟议方法的稳健的有限抽样性能,特别是在估计偏差和信任间隔范围方面,并运用了方法,用巴奇卢斯亚比蒂利斯(Bacillus subtilis)生产里夫拉文的基因表达数据。