
Data Analysis Using Hierarchical Generalized Linear Models with R
- 322 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data Analysis Using Hierarchical Generalized Linear Models with R
About this book
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.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- List of notations
- Preface
- 1 Introduction
- 2 GLMs via iterative weighted least squares
- 3 Inference for models with unobservables
- 4 HGLMs: from method to algorithm
- 5 HGLMs modeling in R
- 6 Double HGLMs - using the dhglm package
- 7 Fitting multivariate HGLMs
- 8 Survival analysis
- 9 Joint models
- 10 Further topics
- References
- Data Index
- Author Index
- Subject Index