Categorical Data Analysis: Regression Models for Count Data · Introduction

Introduction

What this guide covers

A rigorous monograph on the mathematical formulation, parameter estimation, diagnosis and advanced modeling of non-negative integer dependent variables.

Overview

Topics covered

Introduction
Fundamentals of Counting Data and Poisson Distribution
Exposure and Compensation Variables (Offsets)
The Poisson Regression Model
Log-Linear Formulation
Maximum Likelihood (ML) Estimation and IRLS Algorithm

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

P(Y = y) = \frac{e^{-\lambda} \lambda^y}{y!}, \quad y \in \{0, 1, 2, \ldots\}

View LaTeX source

P(Y = y) = \frac{e^{-\lambda} \lambda^y}{y!}, \quad y \in \{0, 1, 2, \ldots\}