In this article, we focus on the estimation of varying-coefficient mixed effects models for longitudinal and sparse functional response data, by using the generalized least squares method coupling a modified local kernel smoothing technique. This approach provides a useful framework that simultaneously takes into account the within-subject covariance and all observation information in the estimation to improve efficiency. We establish both uniform consistency and pointwise asymptotic normality for the proposed estimators of varying-coefficient functions. Numerical studies are carried out to illustrate the finite sample performance of the proposed procedure. An application to the white matter tract dataset obtained from Alzheimer’s Disease Neuroimaging Initiative study is also provided.

中文翻译：

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具有功能响应数据的变系数混合效应模型的有效估计

在本文中，我们通过使用广义最小二乘法与改进的局部核平滑技术相结合，专注于估计纵向和稀疏函数响应数据的变系数混合效应模型。这种方法提供了一个有用的框架，它同时考虑了估计中的主体内协方差和所有观察信息，以提高效率。我们为可变系数函数的拟议估计量建立了统一一致性和逐点渐近正态性。进行了数值研究以说明所提出程序的有限样本性能。还提供了对从阿尔茨海默病神经影像学倡议研究中获得的白质束数据集的应用。