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 many applications, the response variable is not Normally distributed. GLM can be used to analyze data from various non-Normal distributions. In this short course, we will introduce two most common ...

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 paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

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

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 ...