Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate ...

A generalized linear model extends the traditional linear model and is, therefore, applicable to a wider range of data analysis problems. A generalized linear model consists of the following ...

Jun Yan, Robert H. Aseltine, Jr., Ofer Harel, Comparing Regression Coefficients Between Nested Linear Models for Clustered Data With Generalized Estimating Equations, Journal of Educational and ...

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 ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...