E-Book
E-Book, Englisch, 392 Seiten
E-Book, Englisch, 392 Seiten
Reihe: Chapman & Hall/CRC Texts in Statistical Science
ISBN: 978-1-351-72621-4
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them
Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis
Connects Bayesian analysis and MCMC methods to fit GLMs
Contains numerous examples from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods
Offers the data sets and solutions to the exercises online
Describes the components of good statistical practice to improve scientific validity and reproducibility of results.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Dobson / Barnett
An Introduction to Generalized Linear Models jetzt bestellen!
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them
Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis
Connects Bayesian analysis and MCMC methods to fit GLMs
Contains numerous examples from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods
Offers the data sets and solutions to the exercises online
Describes the components of good statistical practice to improve scientific validity and reproducibility of results.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Autoren/Hrsg.
Weitere Infos & Material
Introduction
Model Fitting
Exponential Family and Generalized
Linear Models
Estimation
Inference
Normal Linear Models
Binary Variables and Logistic Regression
Nominal and Ordinal Logistic Regression
Poisson Regression and Log-Linear Models
Survival Analysis
Clustered and Longitudinal Data
Bayesian Analysis
Markov Chain Monte Carlo Methods
Example Bayesian Analyses
Postface
Appendix
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