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书名：An Introduction to Generalized Linear Models，3rd
作者：Annette J. Dobson, Adrian Barnett
出版社：Chapman & Hall/CRC Texts in Statistical Science
主要内容：This books provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. It 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. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
简要目录：
Chapter 1. Introduction
Chapter 2. Model Fitting
Chapter 3. Exponential Family and Generalized Linear Models
Chapter 4. Estimation
Chapter 5. Inference
Chapter 6. Normal Linear Models
Chapter 7. Binary Variables and Logistic REgression
Chapter 8. Nominal and Ordinal Logistic Regression
Chapter 9. Poisson Regression and Log-Linear Models
Chapter10. Survival Analysis
Chapter11. Clustered and Logitudinal Data
Chapter12. Bayesian Analysis
Chapter13. Markov Chain Monte Carlo Methods
Chapter14. Example Bayesian Analyses
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