Scholars usually adopt the method of least squared to model the relationship between a
response variable and two or more explanatory variables. Ordinary least squares estimator's …

The beta regression is a widely known statistical model when the response (or the
dependent) variable has the form of fractions or percentages. In most of the situations in beta …

The linear regression model becomes unsuitable when the response variable is expressed
as percentages, proportions, and rates. The beta regression (BR) model is more appropriate …

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 …

In the linear regression model, the multicollinearity effects on the ordinary least squares
(OLS) estimator performance make it inefficient. To solve this, several estimators are given …

We proposed new two-parameter estimators to solve the problem called multicollinearity for
the logistic regression model in this paper. We have derived these estimators' properties and …

The gamma regression model is widely applied when the response variable is continuous
and positively skewed. In the multicollinearity problem, the usual maximum likelihood …

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 …

Recently, research papers have shown a strong interest in modeling count data. The over-
dispersion or under-dispersion are frequently seen in the count data. The count data …

The Zero‐inflated negative binomial (ZINB) regression models are mainly applied for count
data that shows over‐dispersion and extra zeros. Multicollinearity is considered to be a …