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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. 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.
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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