Data Analysis Using Hierarchical Generalized Linear Models with R

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Data Analysis Using Hierarchical Generalized Linear Models with R by Youngjo Lee, Lars Ronnegard, Maengseok Noh, CRC Press
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Author: Youngjo Lee, Lars Ronnegard, Maengseok Noh ISBN: 9781351811552
Publisher: CRC Press Publication: July 6, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Youngjo Lee, Lars Ronnegard, Maengseok Noh
ISBN: 9781351811552
Publisher: CRC Press
Publication: July 6, 2017
Imprint: Chapman and Hall/CRC
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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