Generalized linear models: count data and log-linear models · Introduction

Introduction

What this guide covers

A rigorous treatise on asymptotic theory, overdispersion modeling, probabilistic structures with excess of zeros, and the decomposition of log-linear space into contingency tables.

Overview

Topics covered

Introduction
Generalized Linear Models for Count and Log-Linear Data
Introduction and Fundamentals of Counting Models
The Poisson Equidispersion Constraint
The Poisson Regression Model
Score Equations and Hessian Derivation

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

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

View LaTeX source

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