GLM - 13 - GEE (Generalized Estimating Equations)

When we're dealing with data coming in groups, GEE extends the GLM likelihood analysis to incorporate a (within group) correlation structure. There are different (plausible) correlation structures: independence, exchangeable, autoregressive, etc. for different scenarios. The correlation structure is estimated by averaging on the Pearson's residuals. The estimation is done in an iterative way - estimate the coefficients, then the correlations, rinse-repeat.

Corrections:
At 03:00 - Panel data is actually a synonym for longitudinal data.
At 8:30 - under-estimation and over-estimation is of-course assuming that rho is positive. If rho is negative the under and over are switched.

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“GLM in R” Course Outline:
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Administration
Up to Scratch
Notebook – Introduction
Notebook – Linear Models
Notebook – Intro to R
Intro to GLM’s
Linear Models vs. Generalized Linear Models
Least Squares vs. Maximum Likelihood
Saturated vs. Constrained Model
Link Functions
Exponential Family
Definition and Examples
More Examples
Notebook – Exponential Family
Mean and Variance
Notebook – Mean-Variance Relationship
Deviance
Deviance
Notebook – Deviance
Likelihood Analysis
Likelihood Analysis
Numerical Solution
Notebook – GLM’s in R
Notebook – Fitting the GLM
Inference
Code Examples:
Notebook – Binary/Binomial Regression
Notebook – Poisson & Negative Binomial Regression
Notebook – Gamma & Inverse Gaussian Regression
Advanced Topics:
Quasi-Likelihood
Generalized Estimating Equations (GEE)
Mixed Models (GLMM)

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