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 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 ...

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, ...

Sankhyā: The Indian Journal of Statistics, Series B (2008-), Vol. 81, Special Issue on Econometrics (September 2019), pp. S146-S200 (55 pages) We propose generalized moments LASSO estimator, combining ...

This is a preview. Log in through your library . Abstract For the problem of variable selection in generalized linear models, we develop various adaptive Bayesian criteria. Using a hierarchical ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...