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.
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.
Produktdetails
Produktdetails
Chapman & Hall/CRC Monographs on Statistics and Applied Probability
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
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
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