Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...

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, ...

is the deviance divided by its associated degrees of freedom. When the scale parameter is a constant specified in the method dialog, or when the response has a Poisson or binomial distribution, the ...

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 ...

is the constant scale parameter specified in the method dialog or a value of 1.0 for maximum-likelihood estimation for Poisson or binomial distributions.

This paper presents an attempt to explicate some common features of several superficially diverse techniques of data analysis and to indicate how the logic of a single abstract model is relevant to ...