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Zusatztext "... an important! useful book! well-written by two authorities in the field..."-Times Higher Education Supplement"... an enormous range of work is covered... represents! perhaps! the most important field of research in theoretical and practical statistics. For all statisticians working in this field! the book is essential."-Short Book Reviews"... this is a rich book; rich in theory! rich in examples! and rich in a statistical sense. I highly recommend it."-Biometrics"... a definitive and unified presentation...by the outstanding experts of this field."-Statistics"This is a wonderful book... Reading the book is like listening to a good lecturer. The authors present the material clearly! and they treat the reader with respect. There is a balance between discussion! mathematical presentation of models! and examples."-Technometrics"... a complete introduction to the topic in a single monograph... a very readable book that provides the reader with great insight into a vast array of data analysis techniques... -Siam Review"... a unique and useful text for intermediate undergraduate teaching."-THES Informationen zum Autor P. McCullagh Klappentext The bestselling second edition of this outstanding text remains popular today for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. It provides a definitive unified, treatment of methods for the analysis of diverse types of data. The authors focus on the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. With its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference. Zusammenfassung This monograph deals with a class of statistical models that generalizes classical linear models to include many other models that have been found useful in statistical analysis. Inhaltsverzeichnis Preface, Introduction, Background, The Origins of Generalized Linear Models, Scope of the Rest of the Book, An Outline of Generalized Linear Models, Processes in Model Fitting, The Components of a Generalized Linear Model, Measuring the goodness of Fit, Residuals, An Algorithm for Fitting Generalized Linear Models, Models for Continuous Data with Constant Variance, Introduction, Error Structure, Systematic Component (Linear Predictor), Model Formulae for Linear Predictors, Aliasing, Estimation, Tables as Data, Algorithms for Least Squares, Selection of Covariates, Binary Data , Introduction, Binomial Distribution, Models for Binary Responses, Likelihood functions for Binary Data, Over-Dispersion, Example, Models for Polytomous Data, Introduction, Measurement scales, The Multinomical Distribution, Likelihood Functions, Over-Dispersion, Examples, Log-Linear Models, Introduction, Likelihood Functions, Examples, Log-Linear Models and Multinomial Response Models, Multiple responses, Example, Conditional Likelihoods, Introduction, Marginal and conditional Likelihoods, Hypergeometric Distributions, Some Applications Involving Binary data, Some Aplications Involving Polytomous Data, Models with Constant Coefficient of Variation, Introduction, The Gamma Distribution, Models with Gamma-distributed Observations, Examples, Quasi-Likelihood Functions, Introduction, Independent Observations, Dependent Observations, Optimal Estimating Functions, Optimality Criteria, Extended Quasi-Likelihood, Joint Modelling of Mean and Dispersion, Introduction, Model Specification, Interaction between Mean and Dispersion Effects, Extended Quasi-Likelihood as a Criterion, Adjustments of the Estimating Equations, Joint Optimum Estimating Equations, Example: The Production of Leaf-Springs for Trucks, Models with Additional Non-Linear Parameters, Introduction, Pa...
