The multicollinearity problem occurrence of the explanatory variables affects the least-
squares (LS) estimator seriously in the regression models. The multicollinearity adverse …

Computational prediction of absolute essential genes using machine learning has gained
wide attention in recent years. However, essential genes are mostly conditional and not …

Bootstrap is a simple, yet powerful method of estimation based on the concept of random
sampling with replacement. The ridge regression using a biasing parameter has become a …

Modeling with the gamma regression model is common when the dependent variable is
positively skewed and follows a gamma distribution. The maximum likelihood (ML) estimator …

A new two-parameter estimator was developed to combat the threat of multicollinearity for
the linear regression model. Some necessary and suficient conditions for the dominance of …

Для решения проблемы мультиколлинеарности в регрессионном анализе может
применяться ранее разработанный автором метод выпрямления искаженных …

abstract The multicollinearity problem occurrence of the explanatory variables affects the
leastsquares (LS) estimator seriously in the regression models. The multicollinearity adverse …