P. McCullagh (University of Chicago, Chicago, Illinois, USA), John A. Nelder (Imperial College, London, UK)

Generalized Linear Models

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Verlag: Taylor & Francis Ltd
• 2 ed
• Seitenzahl: 532
• Erscheinungstermin: 1. August 1989
• Englisch
• Abmessung: 245mm x 159mm x 34mm
• Gewicht: 854g
• ISBN-13: 9780412317606
• ISBN-10: 0412317605
• Artikelnr.: 22252796

Autorenporträt

P. McCullagh

Inhaltsangabe

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
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