Publisher's Synopsis
A comprehensive introduction to generalized linear models, including logistic and Poisson regression
This volume presents a thorough introductory treatment of generalized linear models (GLM). It features useful examples of GLMs at work in a variety of settings ranging from applications in biology and biopharmaceuticals, to engineering and quality assurance.
The authors review the types of problems that support the use of GLMs, then provide an overview of many of the basic concepts of multiple linear regression and nonlinear regression. Fundamental concepts such as least squares and the maximum likelihood estimation procedure are discussed. Confidence intervals estimation, hypothesis testing procedures, and model diagnostic checking techniques such as residual plotting and influence diagnostics are also presented in detail. Further coverage includes:
∗ Model fitting, inference, and diagnostics for nonlinear regression
∗ Coverage of logistic and Poisson regression
∗ Vivid illustrations of model fitting, inference, and diagnostic checking using SAS PROC GENMOD and S–PLUS software
∗ Introduction to generalized estimating equations (GEEs)
Generalized Linear Models provides an in–depth introduction to the subject for graduate students in regression courses and for engineers, scientists, and statisticians who must understand and apply GLMs in their work.
