Abstract
In regression modeling, the occurrence of a strong correlation among predictors has negative consequences for regression estimation. This problem can be solved using a variety of biased methods. From the generalized linear models, the beta regression model is a subset. When the response variable under examination is percentage, the beta regression model is a well-known model in research. Using various theories, a number of biased estimators for overcoming multicollinearity in beta regression models have been developed in the literature. There is a review of recent biased techniques for beta regression models. We can learn more about the performance of these biased estimators by comparing them.
