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This is the second edition of a monograph on generalized linear
models with random effects that extends the classic work of
McCullagh and Nelder. It has been thoroughly updated, with around
80 pages added, including new material on the extended likelihood
approach that strengthens the theoretical basis of the methodology,
new developments in variable selection and multiple testing, and
new examples and applications. It includes an R package for all the
methods and examples that supplement the 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.
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.
This book presents the proceedings of the 2nd Pacific Rim
Statistical Conference for Production Engineering: Production
Engineering, Big Data and Statistics, which took place at Seoul
National University in Seoul, Korea in December, 2016. The papers
included discuss a wide range of statistical challenges, methods
and applications for big data in production engineering, and
introduce recent advances in relevant statistical methods.
This book presents the proceedings of the 2nd Pacific Rim
Statistical Conference for Production Engineering: Production
Engineering, Big Data and Statistics, which took place at Seoul
National University in Seoul, Korea in December, 2016. The papers
included discuss a wide range of statistical challenges, methods
and applications for big data in production engineering, and
introduce recent advances in relevant statistical methods.
This book provides a groundbreaking introduction to the likelihood
inference for correlated survival data via the hierarchical (or h-)
likelihood in order to obtain the (marginal) likelihood and to
address the computational difficulties in inferences and
extensions. The approach presented in the book overcomes
shortcomings in the traditional likelihood-based methods for
clustered survival data such as intractable integration. The text
includes technical materials such as derivations and proofs in each
chapter, as well as recently developed software programs in R
("frailtyHL"), while the real-world data examples together with an
R package, "frailtyHL" in CRAN, provide readers with useful
hands-on tools. Reviewing new developments since the introduction
of the h-likelihood to survival analysis (methods for interval
estimation of the individual frailty and for variable selection of
the fixed effects in the general class of frailty models) and
guiding future directions, the book is of interest to researchers
in medical and genetics fields, graduate students, and PhD (bio)
statisticians.
This is the second edition of a monograph on generalized linear
models with random effects that extends the classic work of
McCullagh and Nelder. It has been thoroughly updated, with around
80 pages added, including new material on the extended likelihood
approach that strengthens the theoretical basis of the methodology,
new developments in variable selection and multiple testing, and
new examples and applications. It includes an R package for all the
methods and examples that supplement the book.
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