This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and random parameter models. Using Stata, the book offers numerous examples to assist you in applying the models to your own data situations.
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Statistical Theory & Methods
Statistics & Computing
Statistics for the Biological Sciences
Reference
Professional

Generalized Estimating Equations provides the first complete treatment of the GEE methodology in all of its variations. With a careful balance of origins, applications, relationships, and interpretation, it offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice and addresses recent trends, including GEE2, EGEE, ALR, and missing value problems. The treatment incorporates examples from SAS, Stata, SUDAAN, and S-Plus and shows how and why their results may differ.
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Statistical Theory & Methods
Statistics for the Biological Sciences
Reference
Professional