Noh M., Ronnegard L., Lee Y. Data Analysis Using Hierarchical Generalized Linear Models with R

Chapman and Hall/CRC, 2017. — 334 p. — ISBN: 978-1138627826.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 analyze various kinds of data using R. It provides a likelihood approach to advanced statistical modeling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modeling 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 modeling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.