Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Samplin...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
WOS: 000457437600004In order to combat multicollinearity, the r - d class estimator was introduced i...
INTRODUCTION Some Examples The Scope of this Book Use of Statistical Software STATISTICAL INFERENCE ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
An important statistical development in the last four decades has been the advancement in the field ...
This work proposes joint modeling of parameters in the biparametric exponential family, including h...
The technique of iterative weighted linear regression can be used to obtain maximum likelihood estim...
In order to combat multicollinearity, the r–d class estimator was introduced in linear and binary lo...
This book is concerned with the use of generalized linear models for univariate and multivariate reg...
Recap: Generalized linear models for univariate responses Recall that generalized linear models (GLM...
Introduction and OverviewThe Nature of Limited Dependent VariablesOverview of GLMsEstimation Methods...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
Abstract: To elicit an informative prior distribution for a normal linear model or a gamma generaliz...
The notes and extensions described below are intended to supplement a graduate-level course in the t...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
WOS: 000457437600004In order to combat multicollinearity, the r - d class estimator was introduced i...
INTRODUCTION Some Examples The Scope of this Book Use of Statistical Software STATISTICAL INFERENCE ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
An important statistical development in the last four decades has been the advancement in the field ...
This work proposes joint modeling of parameters in the biparametric exponential family, including h...
The technique of iterative weighted linear regression can be used to obtain maximum likelihood estim...
In order to combat multicollinearity, the r–d class estimator was introduced in linear and binary lo...
This book is concerned with the use of generalized linear models for univariate and multivariate reg...
Recap: Generalized linear models for univariate responses Recall that generalized linear models (GLM...
Introduction and OverviewThe Nature of Limited Dependent VariablesOverview of GLMsEstimation Methods...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
Abstract: To elicit an informative prior distribution for a normal linear model or a gamma generaliz...
The notes and extensions described below are intended to supplement a graduate-level course in the t...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
WOS: 000457437600004In order to combat multicollinearity, the r - d class estimator was introduced i...
INTRODUCTION Some Examples The Scope of this Book Use of Statistical Software STATISTICAL INFERENCE ...