Generalised estimating equations
Topic of Marginal models, and two responses at once, part of Correlated data
Generalised estimating equations
| Type | Topic |
| Taught in | Marginal models, and two responses at once |
| Part | Correlated data |
| Questions using it | 4 |
Estimating equations \sum_iD_i^\top V_i^{-1}(\mathbf{y}_i-\boldsymbol\mu_i)=\mathbf{0} for a marginal mean model, with a working covariance V_i within each cluster. They are the quasi-score for dependent data; inference uses the sandwich variance, so the working correlation need not be right.
In Marginal models, and two responses at once
The estimator solves the generalised estimating equations, the dependent version of the quasi-score. Liang and Zeger (1986) showed that the fit does not require integrating up to a likelihood. If the working variance happens to equal the truth, the sandwich collapses to the model-based inverse information, which is the best variance available. If it does not, the variance of \hat{\boldsymbol{\beta}} is the sandwich: model-based bread on the outside, the empirical residual covariance in the middle. Packages still call those pieces bread and meat.
Used in
- Taught in Marginal models, and two responses at once
- Questions that use it (4) — C38 · C39 · C40 · C41
- All 6 questions on Marginal models, and two responses at once
See also
Marginal model · Sandwich estimator · Working correlation · Covariance · Quasi-score and quasi-likelihood · Marginal and conditional effects · Warp-breaks data · Two responses, one person