In real-world applications, it is important and desirable to learn a model that performs well on
out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the …

Variable selection methods and model selection approaches are valuable statistical tools
that are indispensable for almost any statistical modeling question. This review first …

We propose a new method for estimation in linear models. The 'lasso'minimizes the residual
sum of squares subject to the sum of the absolute value of the coefficients being less than a …

It has been an amazing time since the publication of the first edition, with vast changes
taking place in the fields of mixed effects models and their applications. At the time when the …

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 …

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects
Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and …

Generalized linear mixed models are a widely used tool for modeling longitudinal data.
However, their use is typically restricted to few covariates, because the presence of many …

We address the important practical problem of how to select the random effects component
in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect …

Finding an unconstrained and statistically interpretable reparameterization of a covariance
matrix is still an open problem in statistics. Its solution is of central importance in covariance …

We propose an ℓ1‐penalized estimation procedure for high‐dimensional linear mixed‐
effects models. The models are useful whenever there is a grouping structure among high …