Generalized Linear Models Generalized Linear Models Course Topics Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, ...

where y i is the response variable for the ith observation. The quantity x i is a column vector of covariates, or explanatory variables, for observation i that is known from the experimental setting ...

A supremum-type statistic, based on partial sums of residuals, is proposed to test the validity of the mean function of the response variable in a generalized linear model. The proposed test does not ...

Linear Models (LM) are one of the most commonly used statistical methods to analyze continuous outcomes. However, many studies in Engineering, Medical Study, Education, etc. involve categorical ...

The EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of ...

General Linear Models An analysis-of-variance model can be written as a linear model, which is an equation that predicts the response as a linear function of parameters and design variables. In ...