Eikosstatistics & data science

Statistical Modeling & Regression · Generalized Linear Models (GLM)

Generalized linear models · Parameter estimation and numerical methods

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Chart from the original guide

Overview

Topics covered

  • Introduction
  • Generalized Linear Models: Estimation and Numerical Methods
  • Historical perspective and limitations of classical regression
  • The Exponential Dispersion Family
  • Fundamental Properties of EDF Moments
  • Formal structure of Generalized Linear Models

Related concepts

Main connections

  • Maximum likelihood estimation
  • Linear algebra & matrices
  • Generalized linear models (GLM)
  • Variance & standard deviation
  • Calculus & differentiation
  • Mean & expected value

Compatible notation

Mathematical sample in LaTeX

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

𝐘=𝐗𝛃+𝛆,𝛆∼𝒩(𝟎,σ2𝐈).
View LaTeX source\mathbf{Y} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\varepsilon}, \qquad \boldsymbol{\varepsilon} \sim \mathcal{N}(\mathbf{0}, \sigma^2 \mathbf{I}).