It performs analysis of variance by using least squares regression to fit general linear models, as described in the section "General Linear Models". Among the statistical methods available in PROC ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

These types of situations can often be modeled well by a large class of regression models called generalized linear models (GLM). We will go over some of the basic statistical concepts of GLM and how ...

We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide ...

Researchers and students of applied statistics and the social and behavioral sciences will find this book indispensable for understanding both general linear model theory and application. The model is ...

Generalized linear models (GLM's) have proved suitable for modeling various kinds of data consisting of exponential family response variables with covariates. Bayesian analysis of such data requires ...

Article Published: 10 April 2013 A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model Christina Loley, Inke R König ...