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 maximum likelihood estimator (MLE) suffers from the instability problem in the presence
of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new …

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 …

The ridge regression model has been consistently demonstrated to be an attractive
shrinkage method to reduce the effects of multicollinearity. The Poisson regression model is …

The method of maximum likelihood flops when there is linear dependency (multicollinearity)
and outlier in the generalized linear models. In this study, we combined the ridge estimator …

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 presence of multicollinearity among the explanatory variables has undesirable effects
on the maximum likelihood estimator (MLE). Ridge estimator (RE) is a widely used estimator …

The Poisson regression is very popular in applied researches when analyzing the count
data. However, multicollinearity problem arises for the Poisson regression model when the …

Multicollinearity problem arises frequently in several modern applications, such as
chemometrics, biology, and other scientific fields. The common feature of the …

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 …