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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. Data Analysis Using Hierarchical Generalized Linear Models with R 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.
- 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
Youngjo Lee is a professor in the department of Statistics at Seoul National University, Korea. His current research interests are extension, application, theory and software developments for HGLMs.
Lars Rönnegård is affiliated with the Microdata Analysis group at Dalarna University, Sweden. His current research interests are applications of HGLMs in genetics and ecology, and computational aspects.
Maengseok Noh is a professor in the Department of Statistics at Pukyong National University, Korea. His current research interests are application and software developments for HGLMs.
