  I present here a simple proof that, under general regularity conditions, the
standard parametrization of generalized linear mixed model is identifiable. The
proof is based on the assumptions of generalized linear mixed models on the
first and second order moments and some general mild regularity conditions,
and, therefore, is extensible to quasi-likelihood based generalized linear
models. In particular, binomial and Poisson mixed models with dispersion
parameter are identifiable when equipped with the standard parametrization.
