The parameters in the Poisson regression model are usually estimated using the maximum
likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or …

This paper considers the estimation of parameters for the Poisson regression model in the
presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using …

The Poisson regression model (PRM) is employed in modelling the relationship between a
count variable (y) and one or more explanatory variables. The parameters of PRM are …

It is known that multicollinearity affects the maximum likelihood estimator (MLE) negatively
when estimating the coefficients in Poisson regression. Namely, the variance of MLE inflates …

The Poisson regression model (PRM) is the standard statistical method of analyzing count
data, and it is estimated by a Poisson maximum likelihood (PML) estimator. Such an …

The Liu estimator has consistently been demonstrated to be an attractive shrinkage method
for reducing the effects of multicollinearity. The Poisson regression model is a well-known …

The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson
regression model (PRM). Since the resulting estimators are sensitive to outliers, different …

Poisson regression is one of the methods to analyze count data and, the regression
parameters are usually estimated using the maximum likelihood (ML) method. However, the …

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE)
efficiency in both the linear regression model and the generalized linear model. Alternative …

Modelling of count data has been of extreme interest to researchers. However, in practice,
count data is often identified with overdispersion or underdispersion. The Conway Maxwell …