Generalized linear models: quasi-likelihood and generalized estimating equations (GEE) · Introduction

Introduction

What this guide covers

Generalized Linear Models (GLM), introduced by Nelder and Wedderburn (1972), were a milestone in the unification of regression analysis for non-Gaussian response variables. However, its theoretical and practical validity is strictly subject to compliance with severe probabilistic restrictions.

Overview

Topics covered

Introduction
Generalized Linear Models, Quasi-likelihood and Generalized Estimating Equations (GEE)
1. Fundamentals and Limitations of Classic GLMs
Moment Structure in the Exponential Family
The Collapse of Assumptions: The Phenomenon of Overdispersion
The Dimension of Correlated Data

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

f(y_i; \theta_i, \phi) = \exp \left\{ \frac{y_i \theta_i - b(\theta_i)}{a(\phi)} + c(y_i, \phi) \right\}

View LaTeX source

f(y_i; \theta_i, \phi) = \exp \left\{ \frac{y_i \theta_i - b(\theta_i)}{a(\phi)} + c(y_i, \phi) \right\}