Generalized Estimating Equations (GEE) are a method for analyzing correlated response data, such as repeated measurements on the same subject or observations clustered within groups. GEE extends the framework of Generalized Linear Models (GLMs) to situations where the assumption of independence between observations is not appropriate.
Category Archives: Counts data
Negative Binomial Regression model (NB2)
Negative binomial regression is used for modeling count variables, usually for over-dispersed count outcome variables (e.g. accounting for the extra-Poisson variation). BESH Stats fits negative binomial regression with the traditional NB2[1] parametrization
Poisson regression (Generalized linear model GLM)
Poisson regression is fundamental to the modeling of count data. It is the simplest count regression model; and similar to regular multiple linear regression except that the dependent variable is an observed count that follows the Poisson distribution.