Generalized Linear Models: Diagnosis, Residuals and Influence · Introduction

Introduction

What this guide covers

A comprehensive and mathematically rigorous treatise on validating assumptions, structural perturbation analysis, deriving residual metrics, evaluating stochastic leverage, and quantifying the influence of individual cases on MLG.

Overview

Topics covered

Introduction
1. Mathematical Foundations of Generalized Linear Models
1.1 The One-Dimensional Exponential Family
1.2 Systematic Component, Link Function and the IRLS Algorithm
2. Residual Typology in Generalized Linear Models
2.1 Pearson residuals

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\}