Li Sai, Cai T Tony, Li Hongzhe
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104.
J Am Stat Assoc. 2022;117(540):1835-1846. doi: 10.1080/01621459.2021.1888740. Epub 2021 Apr 20.
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.
线性混合效应模型广泛应用于分析聚类或重复测量数据。我们提出了一种拟似然方法，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聚类大小可能很大的一般情况。对于固定效应，我们提供了速率最优估计器和有效的推断程序，这些程序不依赖于方差分量的结构信息。我们还研究了一般情况下具有高维固定效应的方差分量估计。这些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，并应用于一项关于异质种群小鼠体重指数与基因多态性标记之间关联的实际研究。
