An Introduction to Generalized Linear Models

Dobson, Annette J.

ISBN 10: 0412311003 / ISBN 13: 9780412311000

Edité par Springer London, Limited, 1990

A propos de cet article

Used book that is in excellent condition. May show signs of wear or have minor defects.

N° de réf. du vendeur 48569200-75

Détails bibliographiques

• Titre: An Introduction to Generalized Linear Models
• Éditeur: Springer London, Limited
• Date de parution: 1990
• Langue: anglais
• ISBN à 10 chiffres: 0412311003
• ISBN à 13 chiffres: 9780412311000
• Reliure: Couverture souple
• Édition: Reprint.
• État de l'article: Very Good

A propos de ce titre

Synopsis

This updated edition provides a unifying framework for many commonly used multivariate statistical methods including multiple regression and analysis of variance or covariance for continuous response data as well as logistic regression for binary responses and log-linear models for counted responses. The theory for these models is developed using the exponential, family of distributions, maximum likelihood estimation and likelihood ration tests. This is followed by information on each of the main types of generalized linear models. The statistical computing program GLIM which was developed to fit these models to data is used extensively and other programs, especially MINITAB, are used to illustrate particular issues. The reader is assumed to have a working knowledge of basic statistical concepts and methods (at the level of most introductory statistics courses) and some acquaintance with calculus and matrix algebra. The main changes from the first edition are that many sections have been extensively rewritten to provide more detailed explanations, GLIM and other programs are explicitly used, and many more numerical examples and exercises have been added. Outline of solutions for selected exercises are given. The methods described in this book are widely applicable for analysing data from the fields of medicine, agriculture, biology, engineering, industrial experimentation, and the social sciences.

Présentation de l'éditeur

An undergraduate-level introduction to the topic of generalized linear models
An Introduction to Generalized Linear Models-a new edition of An Introduction to Statistical Modelling-demonstrates how generalized linear models provide a unifying framework for many commonly used multivariate statistical methods, including multiple regression and analysis of variance or covariance for continuous response data, logistic regression for binary responses, and log-linear models for counted responses. The theory for these models is developed using the exponential family of distributions, maximum likelihood estimation, and likelihood ration tests. Chapters on each of the main types of generalized linear models are included. The statistical computing program GLIM , developed to fit these models to data, is used extensively. Other programs, particularly MINITAB, are used to illustrate particular issues. The student is assumed to have a working knowledge of basic statistical concepts and methods, at the level of most introductory statistics courses, and some acquaintance with calculus and matrix algebra. Methods described in this text are widely applicable for analyzing data from the fields of medicine, agriculture, biology, engineering, industrial experimentation, and the social sciences.