Contents
Is Poisson regression multiple regression?
Poisson regression is similar to regular multiple regression except that the dependent (Y) variable is an observed count that follows the Poisson distribution. Hence, Poisson regression is similar to logistic regression, which also has a discrete response variable.
What is modified Poisson regression?
Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems.
Why are data correlated in a generalized Poisson regression?
Moreover, data may be correlated due to the hierarchical study design or the data collection methods. In this study, we propose a multilevel zero-inflated generalized Poisson regression model that can address both over- and underdispersed count data.
Why do Poisson models fail to fit count data?
Poisson or zero-inflated Poisson models often fail to fit count data either because of over- or underdispersion relative to the Poisson distribution. Moreover, data may be correlated due to the hierarchical study design or the data collection methods.
When to use a zero inflated generalized Poisson model?
In addition to a zero-inflated negative binomial (ZINB) regression model, which can be used in instances of excess zeros and overdispersion in data, the increasingly popular zero-inflated generalized Poisson (ZIGP) models can also be applied to both over- and underdispersed count data [31], [6].
Is there a multilevel zinb regression for overdispersed count?
In addition, Moghimbeigi and Eshraghian et al. [23]introduced a multilevel ZINB (MZINB) regression model for overdispersed count data. Instead of including random effects, several authors have considered marginal models for clustered data with excess zeros.