Statistical Modeling & Regression · Generalized Linear Models (GLM)

Advanced generalized linear models · Theory and practice

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Treatise on mathematical statistics

Overview

Topics covered

  • Introduction
  • Generalized Linear Models: Advanced extensions of GLM
  • Rigorous review of classical GLM and the exponential family
  • 1.1 Components of the classic MLG
  • The problem of overdispersion and quasi-likelihood functions
  • 2.1 Wedderburn quasi-likelihood

Related concepts

Main connections

  • Maximum likelihood estimation
  • Variance & standard deviation
  • Poisson distribution
  • Calculus & differentiation
  • Linear algebra & matrices
  • Mixed & multilevel models (GLMM)

Compatible notation

Mathematical sample in LaTeX

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

f(y;θ,ϕ)=exp(yθ−b(θ)a(ϕ)+c(y,ϕ))
View LaTeX sourcef(y; \theta, \phi) = \exp \left( \frac{y\theta - b(\theta)}{a(\phi)} + c(y, \phi) \right)