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A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)

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Model comparison for nested regression models that are not symbolically nested

For a linear model, say that the full model is $\mathbf{Y} = \mathbf{X} \boldsymbol\beta + \mathbf{e}_f$ and the reduced model is $\mathbf{Y} = \mathbf{R} \boldsymbol\alpha + \mathbf{e}_r$. $\mathbf{X …
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