Technologies have driven big data collection across many fields, such as genomics and
business intelligence. This results in a significant increase in variables and data points …

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 …

The linear regression model is not applicable when the response variable's value comes in
percentages, proportions, and rates, which are restricted to the interval (0, 1). In this …

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

The maximum likelihood (ML) method is used to estimate the unknown Gamma regression
(GR) coefficients. In the presence of multicollinearity, the variance of the ML method …

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 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 …

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