A Note on the Identifiability of Generalized Linear Mixed Models

2014/05/04 by Rodrigo Labouriau, Labouriau, Rodrigo
Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #stat.AP #stat.ME

paper · pdf · doi:10.48550/arxiv.1405.0673

9 pages, no figures

arxiv created 2014/05/04 · arxiv updated 2014/05/06

Abstract

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.

Related

• Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics
• Necessary and sufficient conditions for the identifiability of observation-driven models
• Database for identifiability properties of linear compartmental models
• Inference for generalized additive mixed models via penalized marginal likelihood
• Generalized Identifiability Bounds for Mixture Models with Grouped Samples