The estimation of regression models with censored data using logistic and Tobit models

Atefeh Younesi, Elham Kamangar

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)545-550
Number of pages6
JournalLife Science Journal
Volume10
Issue numberSUPPL. 7
StatePublished - 2013
Externally publishedYes

Keywords

  • Censored data
  • Logistic
  • Regression models
  • Tobit

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Fingerprint

Dive into the research topics of 'The estimation of regression models with censored data using logistic and Tobit models'. Together they form a unique fingerprint.

Cite this