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 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 Linear regression model explores the relationship between a response variable and
one or more independent variables. The parameters in the model are often estimated using …

Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood
estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the …

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 Poisson regression model (PRM) is usually applied in the situations when the
dependent variable is in the form of count data. For estimating the unknown parameters of …

The ordinary least square (OLS) estimator is the Best Linear Unbiased Estimator (BLUE)
when all linear regression model assumptions are valid. The OLS estimator, however …

This article proposes another general class of biased estimators which includes some
popular estimators as special cases and discusses its properties for multiple linear …

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

To circumvent the problem of multicollinearity in regression models, a ridge‐type estimator is
recently proposed in the literature, which is named as the Kibria–Lukman estimator (KLE) …