关于广义线性混合模型可辨识性的一个注记
A Note on the Identifiability of Generalized Linear Mixed Models
摘要：
本文给出了一个简单的证明，证明了在一般的正则条件下，广义线性混合模型的标准参数化是可识别的。该证明基于广义线性混合模型关于一阶和二阶矩的假设以及一些一般的弱正则条件，因此可推广到基于拟似然的广义线性模型.特别是，二项和泊松混合模型与分散参数时，配备标准参数化是可识别的。
英文摘要：
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.
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