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Data analysis using hierarchical generalized linear models with R

By: Contributor(s): Material type: TextTextPublication details: London CRC Press 2017Description: xii, 321 pISBN:
  • 9781138627826
Subject(s): DDC classification:
  • 519.5 L3D2
Summary: 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. https://www.crcpress.com/Data-Analysis-Using-Hierarchical-Generalized-Linear-Models-with-R/Lee-Ronnegard-Noh/p/book/9781138627826
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 28-B / Slot 1414 (0 Floor, East Wing) Non-fiction General Stacks 519.5 L3D2 (Browse shelf(Opens below)) Available 196389

A Chapman & Hall book.

Includes bibliographical references and index.

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.


https://www.crcpress.com/Data-Analysis-Using-Hierarchical-Generalized-Linear-Models-with-R/Lee-Ronnegard-Noh/p/book/9781138627826

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