Generalized linear models. With applications in engineering and the sciences (Q2762737)

This is an introductory textbook on generalized linear models (GLM). It is intended for anyone who has completed a course in regression analysis. The book has the following remarkable features: NEWLINENEWLINENEWLINEa) The authors give a thorough treatment of logistic and Poisson regression as special cases of GLM, and after that the general case is nicely understandable. b) An introduction to generalized estimating equations is given, a topic closely related to GLM where correlation structures are assumed within large experimental units. c) Many examples of GLMs are considered coming from biology, biopharmaceutics, engineering, quality assurance, and design of experiments. d) Two software packages are used to illustrate every aspect of model fitting, inference, and diagnostic checking. NEWLINENEWLINENEWLINEThe book is written at a high methodical level, the presentation is clear and transparent. Each chapter is finished with exercises containing both theoretical and practical problems. There are other excellent works on GLM, e.g., \textit{P. McCullagh} and \textit{J.A. Nelder}, Generalized linear models. (1983; Zbl 0588.62104), but most of them use more advanced mathematics and are aimed primarily at theoretical statisticians. The book under review will allow a broad group of engineers, scientists and statisticians to learn about GLM, including both the theory and applications. NEWLINENEWLINENEWLINEContents: Ch. 1, Introduction to GLMs. Ch. 2, Linear regression models. Ch. 3, Nonlinear regression models. Ch. 4, Logistic and Poisson regression models. Ch. 5, The family of GLMs. Ch. 6, Generalized estimating equations. Ch. 7, Further advances and applications in GLM.