Main Takeaway: When we're dealing with data coming in groups, GEE extends the GLM likelihood analysis to incorporate a (within group) ... In this video we discuss the framing of GEE's using M-estimators and how this gives us useful asymptotic results!
012 Generalized Estimating Equations Estimating Parameters From Marginal Models -
When we're dealing with data coming in groups, GEE extends the GLM likelihood analysis to incorporate a (within group) ... In this video we discuss the framing of GEE's using M-estimators and how this gives us useful asymptotic results! In this video we discuss GEEs for continuous, binary, and count data, setting up the
Important details found
- When we're dealing with data coming in groups, GEE extends the GLM likelihood analysis to incorporate a (within group) ...
- In this video we discuss the framing of GEE's using M-estimators and how this gives us useful asymptotic results!
- In this video we discuss GEEs for continuous, binary, and count data, setting up the
- Trevor Hastie and Rob Tibshirani interview Scott Zeger about his work on
Why this topic is useful
This format is designed to help readers move from a broad question into more specific pages without losing context.
Frequently Asked Questions
What is this page about?
This page summarizes 012 Generalized Estimating Equations Estimating Parameters From Marginal Models and connects it with related entries, references, and supporting context.
Is the information always complete?
Not always. Some topics may need verification from official or primary sources.
How should readers use this information?
Use it as a starting point, then open related pages for more specific details.
