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Beta regression model is useful in the analysis of bounded continuousoutcomes such as proportions. It is well known that for any regression model,the presence of multicollinearity leads to poor performance of the maximumlikelihood estimators. The ridge type estimators have been proposed toalleviate the adverse effects of the multicollinearity. Furthermore, when someof the predictors have insignificant or weak effects on the outcomes, it isdesired to recover as much information as possible from these predictorsinstead of discarding them all together. In this paper we proposed ridge typeshrinkage estimators for the low and high dimensional beta regression model,which address the above two issues simultaneously. We compute the biases andvariances of the proposed estimators in closed forms and use Monte Carlosimulations to evaluate their performances. The results show that, both in lowand high dimensional data, the performance of the proposed estimators aresuperior to ridge estimators that discard weak or insignificant predictors. Weconclude this paper by applying the proposed methods for two real data fromeconometric and medicine.

Ridge-Type Shrinkage Estimators in Low and High Dimensional Beta Regression Model with Application in Econometrics and Medicine