The Esti
The Estimation of Regression Models with Censored Data Using Logistic and Tobit Models
Atefeh younesi a, Elham Kamangar b
a Department of Mathematics, Master of Science, Zanjan University, Zanjan, Iran
b Department of Mathematics, Payam Noor University of Tehran
Abstract: In statistics, censoring occurs when the value of an observation is only partially known. The aim of this paper is estimation of a regression model with censored data using Logistic and Tobit models. We have compared two models based on goodness of fit and forecasting accuracy criteria. We have used the data of rate of return and volatility of Tehran Stock Exchange. Results indicate that Based on Akaike info criterion, Schwarz criterion, Hannan-Quinn criterion and Log likelihood, the model of Tobit has better goodness of fit than Logistic model. Criteria of RMSE and MAE indicate that the Tobit model has more accuracy of forecasting than Logistic Model.
[Atefeh younesi, Elham Kamangar. The Estimation of Regression Models with Censored Data Using Logistic and Tobit Models. Life Sci J 2013;10(7s):545-550] (ISSN:1097-8135). http://www.lifesciencesite.com. 85
Keywords: Regression Models, Censored Data, Logistic, Tobit.
