Overview: An Introduction to Generalized Linear Models, 4th Edition by Annette J. Dobson and Adrian G. Barnett presents a unified theoretical and applied framework for statistical modeling across continuous, binary, count, categorical, correlated, and time-to-event data. It develops exponential family distributions, link functions, maximum likelihood estimation, and model diagnostics before covering normal linear models, binary logistic regression, nominal and ordinal logistic regression, Poisson and log-linear models, survival analysis, generalized estimating equations (GEE), mixed models for longitudinal data, and Bayesian generalized linear models using Markov chain Monte Carlo (MCMC) methods, with worked examples in R, Stata, and WinBUGS.
About This 4th Edition
Who Uses This Book
- Upper-Level Undergraduate and Graduate Statistics (MS/PhD) Students
- Biostatistics and Epidemiology (MPH/MS) Students
- Data Scientists and Quantitative Analysts
- Biomedical, Public Health, and Clinical Researchers
- Econometrics, Social Science, and Behavioral Researchers
- Actuarial Science and Risk Modeling Professionals
- Applied Statisticians Using R, Stata, or Bayesian Modeling Tools
- Statistics and Biostatistics Faculty and Course Instructors
Also suitable for other researchers and quantitative practitioners who need a rigorous yet practical reference linking generalized linear model theory, estimation, model selection, and software implementation.
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