International Journal of Statistics in Medical and Biological Research (Volume 1 Issue 1)
 A Comparison of Two Methods for Estimating Censored Linear Regression Models abstract
Pages 1-8

Ersin Yılmaz and Dursun Aydın


Published: 30 August 2017
This paper presents two basic methods called as weighted least squares (WLS) and synthetic data transformations (SDT). The key idea of the paper is to estimate the parameters of the linear regression model with randomly right-censored data by using these two methods. Recently, the mentioned methods have received considerable attention in the literature. Studies on this subject show that both methods work well for linear regression model with censored data. A particular focus of our paper is to compare the performance of the WLS and SDT methods and to reveal the strong and weak aspects of them. In this context, we made a simulation study and a real data example.
Least squares, Kaplan-Meier estimator, linear regression, right-censored data, synthetic data, Kaplan-Meier weights.