Abstract

Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional fixed effects. The proposed method is applicable to general settings where the dimension of the random effects and the cluster sizes are possibly large. Regarding the fixed effects, we provide rate optimal estimators and valid inference procedures that do not rely on the structural information of the variance components. We also study the estimation of variance components with high-dimensional fixed effects in general settings. The algorithms are easy to implement and computationally fast. The proposed methods are assessed in various simulation settings and are applied to a real study regarding the associations between body mass index and genetic polymorphic markers in a heterogeneous stock mice population.

摘要

线性混合效应模型广泛用于分析聚类或重复测量数据。我们提出了一种准似然方法，用于估计和推断具有高维固定效应的线性混合效应模型中的未知参数。所提出的方法适用于随机效应维度和簇大小可能很大的一般设置。关于固定效应，我们提供了速率最优估计器和不依赖于方差分量的结构信息的有效推理程序。我们还研究了一般设置中具有高维固定效应的方差分量的估计。该算法易于实现且计算速度快。所提出的方法在各种模拟设置中进行了评估，并应用于关于异质小鼠群体中体重指数和遗传多态性标记之间的关联的真实研究。