ABSTRACT

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing.

This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.

chapter 1|18 pages

Introduction

chapter 2|18 pages

GLMs via Iterative Weighted Least Squares

chapter 3|28 pages

Inference for Models with Unobservables

chapter 4|30 pages

HGLMs: From Method to Algorithm

chapter 5|42 pages

HGLMs Modeling in R

chapter 6|40 pages

Double HGLMs - Using the dhglm Package

chapter 7|46 pages

Fitting Multivariate HGLMs

chapter 8|30 pages

Survival Analysis

chapter 9|18 pages

Joint Models

chapter 10|28 pages

Further Topics