Analysis of deviance for nested generalized linear models

The deviance of a fitted generalized linear model isFor regular nested models, the deviance reduction $D_0-D_1=2({\ell }_1-{\ell }_0)$ is asymptotically chi-squared with degrees of freedom equal to the difference in fitted dimensions.

The unscaled residual deviance of an exponential dispersion family compares its fitted mean with the saturated statistical model at a common dispersion parameter. Its normalization removes the factor $1/\varphi$ in the log-likelihood. In Poisson regression and binomial regression, $\varphi =1$, so it is twice the saturated-minus-fitted log-likelihood. Under suitable regularity, $D/\varphi$ has approximately a chi-squared distribution with the residual degrees of freedom; this is not an automatic accurate approximation for sparse responses. Nested deviance reductions with estimated dispersion lead to approximate F-tests after scaling by the dispersion estimate.