Data Analysis Using Hierarchical Generalized Linear Models with R by Youngjo Lee

Data Analysis Using Hierarchical Generalized Linear Models with R by Youngjo Lee

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.

"Data Analysis Using Hierarchical Generalized Linear Models with R by Lee et al is an advanced book on regression and mixed effects statistical modelsThe book presents a class of generalized linear models (GLMs) with random effects. In hierarchical generalized linear models (HGLMs), the random effects might enter in the location parameter, in the dispersion parameter, or in both. These extensions cover a vast number of statistical problems containing unobservable random variables, including missing data, latent variables, and predictions. The book presents an endless volume of case studies, using a bundle of R packages for implementation: hglm, dhglm, mdhglm, frailtyHL and jointdhglm. …The authors concentrate on the practical aspects of HGLMs, and show how improvements in numerical methods (e.g., Laplace approximations to integrals) allow HGLMs to be used in practice. The diversity of statistical models covered in this book is fascinating. ...In general, the authors have presented a good balance between theory and practical applications in R."
-Pablo Emilio Verde, ISCB Jun2 2018

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.

SKU	Unavailable
ISBN 13	9781138627826
ISBN 10	1138627828
Title	Data Analysis Using Hierarchical Generalized Linear Models with R
Author	Youngjo Lee
Condition	Unavailable
Publisher	Taylor & Francis Ltd
Year published	2017-06-07
Number of pages	334
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable