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

Year 2019, Volume: 20 , 1 - 12, 16.12.2019

Dursun Aydın , Ersin Yılmaz

https://doi.org/10.18038/estubtda.632694

Abstract

In this paper, we
introduce three different data transformation approaches such as synthetic data
transformation ([1]; [2]; [3]), Kaplan-Meier weights ([4];  [5] ; [6]) and k-nearest neighbour (kNN)
imputation method ([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 (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

References

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