论文标题
半参数混合效应模型，用于具有非正态误差的纵向数据
Semiparametric Mixed-effects Model for Longitudinal Data with Non-normal Errors
论文作者
论文摘要
如果线性预测器组件中存在非参数函数，则使用混合效应模型分析纵向数据时可能会出现困难。这项研究扩展了在响应变量并不总是遵循正态分布并且非参数组件构成的添加剂模型的情况下，在响应变量并不总是遵循正态分布的情况下，使用半参数混合效应建模。提出了一种新的方法，以使用具有两个惩罚项的双含量的广义估计方程来识别明显的线性和非线性组件。此外，迭代方法通过合并工作协方差矩阵来提高估计回归系数的效率。所得估计量的甲骨文特性是在某些规律性条件下建立的，其中参数和非参数组件的尺寸随着样本量的增长而增加。我们进行数值研究以证明我们的提议的功效。
Difficulties may arise when analyzing longitudinal data using mixed-effects models if there are nonparametric functions present in the linear predictor component. This study extends the use of semiparametric mixed-effects modeling in cases when the response variable does not always follow a normal distribution and the nonparametric component is structured as an additive model. A novel approach is proposed to identify significant linear and non-linear components using a double-penalized generalized estimating equation with two penalty terms. Furthermore, the iterative approach provided intends to enhance the efficiency of estimating regression coefficients by incorporating the calculation of the working covariance matrix. The oracle properties of the resulting estimators are established under certain regularity conditions, where the dimensions of both the parametric and nonparametric components increase as the sample size grows. We perform numerical studies to demonstrate the efficacy of our proposal.
