Data analysis using hierarchical generalized linear models with R

✍ Scribed by Youngjo Lee, Lars Ronnegard, Maengseok Noh

• Publisher: Chapman and Hall/;CRC Press
• Year: 2017
• Tongue: English
• Leaves: 334
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content: Introduction. GLMs via iterative weighted least squares. Inference for models with unobservables. HGLMs: from Method to Algorithm. HGLM modelling in R. Double HGLMS - using the dhglm package. Fitting multivariate HGLMs. Survival analysis. Joint models. Further Topics.

✦ Subjects

Linear models (Statistics) -- Textbooks.;Multilevel models (Statistics) -- Textbooks.;R (Computer program language);Linear models (Statistics);Multilevel models (Statistics)