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, ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

The Annals of Statistics, Vol. 49, No. 2 (April 2021), pp. 702-722 (21 pages) For a nonlinear regression model, the information matrices of designs depend on the parameter of the model. The adaptive ...

Obtaining estimates that are nearly unbiased has proven to be difficult when random effects are incorporated into a generalized linear model. In this paper, we propose a general method of adjusting ...

Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...

Cover -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgments -- 1 Introduction and Background -- 1.1 Introduction -- 1.2 What This Book Is Not About ...