Data Analysis Using Hierarchical Generalized Linear Models with R
Published on 6. July 2017
334 pages
978-1-351-81156-9 (ISBN)
Description
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 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.
Reviews / Votes
"Data Analysis Using Hierarchical Generalized Linear Models with R by Lee et al is an advanced book on regression and mixed effects statistical models. The 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
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
71 s/w Abbildungen, 53 s/w Tabellen, 37 s/w Photographien bzw. Rasterbilder, 34 s/w Zeichnungen
53 Tables, black and white; 34 Line drawings, black and white; 37 Halftones, black and white; 71 Illustrations, black and white
File size
22,99 MB
ISBN-13
978-1-351-81156-9 (9781351811569)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Youngjo Lee | Lars Ronnegard | Maengseok Noh
Data Analysis Using Hierarchical Generalized Linear Models with R
Book
09/2020
1st Edition
Chapman & Hall/CRC
€59.41
Shipment within 15-20 days
Youngjo Lee | Lars Ronnegard | Maengseok Noh
Data Analysis Using Hierarchical Generalized Linear Models with R
Book
06/2017
1st Edition
CRC Press
€148.56
Shipment within 10-20 days
Persons
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 Roennegard 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.
Lars Roennegard 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.
Content
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.
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.
