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 …

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 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 …

Regression analyses look at how the answer variable and one or more explanatory factors
are related. When the explanatory variables are linearly dependent, it becomes practically …

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 …

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 …

The Liu-type estimator has been consistently demonstrated to be an attractive shrinkage
method to reduce the effect of multicollinearity problem. It is known that multicollinearity …

In the field of chemical data modeling, it is common to encounter response variables that are
constrained to the interval (0, 1). In such cases, the beta regression model is often a more …

It is a challenge in the real application when modelling the relationship between the
response variable and several explanatory variables when the existence of collinearity …

A biased estimator, compared to least squares estimators, is one of the most used statistical
procedures to overcome the problem of multicollinearity. Liu-type estimators, which are …