عنوان مقاله [English]
Identifiablity is a necessary property for the adequacy of a statistical model. When a model is not identifiable, no amount of data cannot determine true parameter. In this article, well-known concept of identifiablity and it’s properties is reviewed. Moreover, since non-identifiablity problem in linear mixed effects models and generalized linear models with random effects is very common, our main focus is on these models. On the other hand, statistical software, after fitting non-identifiable models, don’t usually indicate the problem and show invalid outputs. Consequently, it is useful to have a way to check model identifiability before fitting. In this regard, some new theorems to check identifiability in generalized linear models with random effects are presented. data from non-identifiable models are simulated and problems with model non-identifiablity are listed for showing advantages of the mentioned theorems.