Linear mixed effects models are highly flexible in handling a broad range of data types and
are therefore widely used in applications. A key part in the analysis of data is model …

Cortical thickness (CT) and surface area (SA) vary widely between individuals and are
associated with intellectual ability and risk for various psychiatric and neurodevelopmental …

Linear mixed-effects models are a class of models widely used for analyzing different types
of data: longitudinal, clustered and panel data. Many fields, in which a statistical …

Individual differences in neuroanatomy are associated with intellectual ability and
psychiatric risk. Factors responsible for this variability remain poorly understood. We tested …

This article studies model averaging for linear mixed-effects models. We establish an
unbiased estimator of the squared risk for the model averaging, and use the estimator as a …

Variance components estimation and mixed model analysis are central themes in statistics
with applications in numerous scientific disciplines. Despite the best efforts of generations of …

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 …

Introduction: Recently, new clinical trial designs involving biomarkers have been studied
and proposed in cancer clinical research, in the hope of incorporating the rapid growing …

In this paper, we discuss the selection of random effects within the framework of generalized
linear mixed models (GLMMs). Based on a reparametrization of the covariance matrix of …

In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and
slab priors is widely adopted to select variables and estimate unknown parameters …