P. McCullagh

Generalized Linear Models (eBook, PDF)

• Format: PDF

Produktbeschreibung

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 532
• Erscheinungstermin: 22. Januar 2019
• Englisch
• ISBN-13: 9781351445856
• Artikelnr.: 54956986

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
Pa