Hierarchical generalized linear model

Hierarchical generalized linear models(HGLM) can be considered as an extension to generalized linear models. In generalized linear models, the error components are assumed to be independent. But hierarchical generalized linear model allows different error components. The error components can be correlated and not necessarily follow Normal distribution. When observations belong to different clusters, the observations in the same cluster are positively correlated because observations in the same cluster share some common features. In this situation, using generalized linear models and ignoring the correlation may cause problems.

Overview and Model

Model

In a hierarchical model, observations are grouped into clusters, and the distribution of an observation is determined not only by common structure among all clusters but also by the specific structure of the cluster where this observation belongs. As a consequence, random effect component is introduced into the model.

Identifiability

Identifiability in a concept in statistics. In the model stated above, the location of v is not identifiable. In order to make the model identifiable, we need to impose constraints on parameters. The constraint is usually imposed on random effects, such as E(v)=0.

Models with different distributions and link functions

By assuming different distributions of y|u and u, and using different functions of g and v, we will be able to obtain different models. In hierarchical generalized linear models, the distributions of random effect u do not necessarily follow normal distribution. If the distribution of u is normal and the link function of v is the identity function, then hierarchical generalized linear model is the same as generalized linear mixed model.

Distributions of y|u and u can also be chosen to be conjugate, since nice properties hold and it is easier for computation and interpretation. For example, if where , , and canonical log link is used, then we call the model Poisson conjugate HGLM. If follows binomial distribution with , u has the conjugate beta distribution, and canonical logit link is used, then we call the model Beta conjugate model. Moreover, the mixed linear model is in fact the normal conjugate HGLM.

A summary of commonly used models are:

Commonly used models

Model name	distribution of y	Link function between y and u	distribution of u	Link function between u and v
Normal conjugate	Normal	Identity	Normal	Identity
Binomial conjugate	Binomial	Logit	Beta	Logit
Poisson conjugate	Poisson	Log	Gamma	Log
Gamma conjugate	Gamma	Reciprocal	Inv-gamma	Reciprocal
Binomial GLMM	Binomial	Logit	Normal	Identity
Poisson GLMM	Poisson	Log	Normal	Identity
Gamma GLMM	Gamma	Log	Normal	Identity

Fitting the hierarchical generalized linear models

Hierarchical generalized linear models are used when observations come from different clusters. There are two types of estimators: fixed effect estimators and random effect estimators, corresponding to parameters in : $\eta =\mathbf {x} {\boldsymbol {\beta }}$ and in $\mathbf {v(u)}$ , respectively. There are different ways to obtain parameter estimates for a HGLM. If only fixed effect estimators are of interests, the population-averaged model can be used. If inference is focused on individuals, random effects will have to be estimated.