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Statistical Modeling & Regression · Generalized Linear Models (GLM)

Generalized Linear Models: Computational Aspects and Implementation

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Advanced Statistical Modeling · Computational Aspects

Overview

Topics covered

  • Introduction
  • The Uniparametric Exponential Family
  • Log-Partitioner Properties
  • GLM Structural Architecture
  • Canonical Link Functions
  • The Likelihood Function and Scoring Equations

Related concepts

Main connections

  • Maximum likelihood estimation
  • Linear algebra & matrices
  • Variance & standard deviation
  • Generalized linear models (GLM)
  • Binomial distribution
  • Calculus & differentiation

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)