Figure 1: A GWAS for a simulated trait with two causal SNPs randomly chosen from a real A. thaliana SNP data set. GWAS data were simulated by adding phenotypic effects to real genotypic data from A.

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

This course will discuss the concept of random effects, why they are called random effects and how they are incorporated in the framework of mixed models. The primary focus of the course will be to ...

Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have ...