SEMIPARAMETRIC REGRESSION ESTIMATES BASED ON SOME TRANSFORMATION TECHNIQUES FOR RIGHT-CENSORED DATA

Authors : Dursun AYDIN, Ersin YILMAZ

Pages : 1-12

Doi:10.18038/estubtda.632694

View : 25 | Download : 9

Publication Date : 2019-12-16

Article Type : Research Paper

Abstract :In this paper, we introduce three different data transformation approaches such as synthetic data transformation insert ignore into journalissuearticles values([1]; [2]; [3]);, Kaplan-Meier weights insert ignore into journalissuearticles values([4];  [5] ; [6]); and k-nearest neighbour insert ignore into journalissuearticles values(kNN); imputation method insert ignore into journalissuearticles values([7]); which are commonly used in censored data applications. The aforementioned approaches are particularly useful when one deals with censored data. The key idea expressed here is to find the smoothing spline estimates for the parametric and nonparametric components of a semiparametric regression model with right-censored data. The estimation is then carried out based on the modified insert ignore into journalissuearticles values(or transformed); data set obtained via these transformation techniques. In order to compare the outcomes of three approaches in semi-parametric regression setting, we carried out a simulation study. According to the results of the simulation, it can be said that the Kaplan-Meier weights have been very successful in dealing with censored observations.
Keywords : right censored, semi parametric regression, imputation, synthetic data, Kaplan Meier weights