The probit regression model (PRORM) aims to model a binary response with one or more
explanatory variables. The parameter of the PRORM is estimated using an estimation …

The Poisson regression model (PRM) is a widely used statistical technique for analyzing
count data. However, when explanatory variables in the model are correlated, the estimation …

Count regression models, particularly negative binomial regression (NBR), are widely used
in various fields, including biometrics, ecology, and insurance. Over-dispersion is likely …

The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression
model used for over-and under-dispersion cases. In regression analysis, when the …

Beta regression (BR) models provide an adequate approach for modeling continuous
outcomes of limited intervals (0, 1). The BR model assumes that the dependent variable …

The beta regression model is a flexible model, which widely used when the dependent
variable is in ratios and percentages in the range of (0.1). The coefficients of the beta …

Abstract The extended Poisson-Tweedie (EPT) regression model is one of the count data
regression models. It's a more flexible model in count data; since EPT model can be used in …

Recent studies focus on modelling count data, which often shows overdispersion or
underdispersion. The Conway–Maxwell–Poisson (COMP) regression model effectively …

The negative binomial regression model is a widely adopted approach when dealing with
dependent variables that consist of non-negative integers or counts. This model serves as …

abstract The Conway–Maxwell Poisson (COMP) regression model is one of the count data
models to account for over–and under–dispersion. In regression analysis, when the …