"First edition published in 2006." 1. Introduction -- What are linear mixed models (LMMs)? -- Models with random effects for clustered data -- Models for longitudinal or repeated-measures data -- A ...

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory ...

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...

Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose ...

Just as PROC GLM is the flagship procedure for fixed-effect linear models, the MIXED procedure is the flagship procedure for random- and mixed-effect linear models. PROC MIXED fits a variety of mixed ...

HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear (and ...