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 give examples of the Poisson-gamma, binomial-beta and gamma-inverse gamma hierarchical generalized linear models. A resolution is proposed for the apparent difference between population-averaged ...

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 ...

As in the case of traditional linear models, fitted generalized linear models can be summarized through statistics such as parameter estimates, their standard errors, and goodness-of-fit statistics.

Observations of multiple-response variables across space and over time occur often in environmental and ecological studies. Compared to purely spatial models for a single response variable in the ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

Topics include: general theory of regression and generalised linear models, linear regression, logistic regression for binary data, models for ordered and unordered (nominal) responses, log-linear ...

This report presents a comprehensive approach to statistical modeling of post-earthquake ignitions and to data compilation for such modeling, and applies it to present day California.