In the past decade, mixed-effects modeling has received a great deal of attention in applied and theoretical statistical literature. They are very flexible tools analyzing repeated measures, panel data, cross-sectional hierarchical data. However, complex nature these models motivated researchers to study different aspects this problem. One which is test significance random effects used model un...

Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...

This paper provides a non-technical introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects. A worked-out example of how to use recent software for mixed-effects modeling is provided. Simulation studies illustrate the advantages offered by mixedeffects analyses compared to traditional analyses based on quasi-F tests, b...

BENJAMIN R. SAVILLE: Bayesian Multilevel Models and Medical Applications. (Under the direction of Dr. Amy Herring.) Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. We propose ...

Motivated by genome-wide association studies we consider a standard linear model with one additional random effect in situations where many predictors have been collected on the same subjects and each predictor is analyzed separately. Three novel contributions are (1) a transformation between the linear and log-odds scales which is accurate for the important genetic case of small effect sizes; ...

The sommer package has been developed to provide R users with a powerful mixed model solver for different genetic and non-genetic analysis in diploid and polyploid organisms. This package allows the user to estimate variance components for a mixed model with the advantage of specifying the variance-covariance structure of the random effects and obtain other parameters such as BLUPs, BLUEs, resi...

Topic variance has a greater effect on performances than system variance but it cannot be controlled by system developers who can only try to cope with it. On the other hand, system variance is important on its own, since it is what system developers may affect directly by changing system components and it determines the differences among systems. In this paper, we face the problem of studying ...