Nature

Generalized linear mixed models for mapping multiple quantitative trait loci

Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...
JSTOR Daily

An Approximate Generalized Linear Model with Random Effects for Informative Missing Data

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...
JSTOR Daily

Bayesian Variable Selection for High Dimensional Generalized Linear Models: Convergence Rates of the Fitted Densities

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...
Nature

A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model

Genome-wide association studies (GWAS) are widely used to uncover novel genetic susceptibility loci for complex genetic diseases. If the true genetic model is known, the association test reflecting ...
Simon Fraser University

Examples of Generalized Linear Models

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...