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 ...

Various methods have been proposed for smoothing under the monotonicity constraint. We review the literature and implement an approach of monotone smoothing with B-splines for a generalized linear ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth ...

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 ...

A research team has developed a machine learning model that allows scientists to reconstruct neuronal circuitry by measuring signals from the neurons themselves. The team constructed an analytical ...

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...