A New Tobit Ridge-Type Estimator of the Censored Regression Model With Multicollinearity Problem - Dawoud, Issam; Abonazel, Mohamed R.; Awwad, Fuad A.; Tag Eldin, Elsayed (Frontiers in Applied Mathematics and Statistics, 2022)
In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem. To reduce this problem effects, the Tobit ridge and the Tobit Liu estimators are proposed. Therefore, this paper proposes a new kind of the Tobit estimation called the Tobit new ridge-type (TNRT) estimator. Also, the TNRT estimator is theoretically compared with the Tobit maximum likelihood, the Tobit ridge, and the Tobit Liu estimators via the mean squared error criterion. Moreover, we perform a Monte Carlo simulation to study the performance of the TNRT estimator compared with the previously defined estimators. And, we use the Mroz dataset to confirm the theoretical and the simulation study results.