Ridge regression is a popular parameter estimation method used to address the collinearity
problem frequently arising in multiple linear regression. The formulation of the ridge …

An examination of mathematical formulations of ridge-regression-type estimators points to a
curious observation: estimators can be derived by both Bayesian and Frequentist methods …

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

In this article, we present a new general class of biased estimators which includes some
popular estimators as special cases and discuss its properties for multiple linear regression …

Regression analysis is a statistical process that utilizes two or more predictor variables to
predict a response variable. When the predictors included in the regression model are …

Regression techniques are generally used to predict a response variable using one or more
predictor variables. In many fields of study, the regressors can be highly intercorrelated …

It is common for a linear regression model that the error terms display some form of
heteroscedasticity and at the same time, the regressors are also linearly correlated. Both of …

The article proposes three modified percentile estimators for parameter estimation of the
Pareto distribution. These modifications are based on median, geometric mean and …

There is a long history of interest in modeling Poisson regression in different fields of study.
The focus of this work is on handling the issues that occur after modeling the count data. For …

The identification of influential observations is an essential element in regression analysis
as they posed a threat to the model building process. The existence of multicollinearity …