An Introduction to Generalized Linear Models (4th Ed.) Chapman & Hall/CRC Texts in Statistical Science Series
Auteurs : Dobson Annette J., Barnett Adrian G.
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.
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.
Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.
Adrian G. Barnett is a professor at the Queensland University of Technology.
Date de parution : 04-2018
15.6x23.4 cm
Date de parution : 04-2018
15.6x23.4 cm
Thèmes d’An Introduction to Generalized Linear Models :
Mots-clés :
generalized linear models; regression; Annette J; Dobson; Bayesian analysis; Markov Chain Monte Carlo; data analysis; goodness-of-fit; Bayesian methods; Big Data; clustered data; logistic regression; survivial analysis; John Nelder; Adrian G; Barnett; Log Likelihood Function; Multiple Linear Regression; Maximum Likelihood Estimator; Nominal Logistic Regression; Log Linear Models; Posterior Interval; Schistosoma Japonicum; Prevalence Ratios; Posterior Probability; Initial White Blood Cell Count; Beetle Mortality; Covariate Patterns; Weibull Distribution; Poisson Regression; Deviance Residuals; Chi Squared Distribution; Newton Raphson Algorithm; Cumulative Logit Model; Logit Link; Covariance Pattern Models; Accelerated Failure Time Models; Proportional Odds Model; WinBUGS Code; Empirical Survivor Function
