KERNEL SMOOTHING AS AN IMPUTATION TECHNIQUE FOR RIGHT-CENSORED DATA
Abstract
Imputation of right-censored observations is an important problem in statistics and other applied sciences. Since right-censored data sets are common in medical studies and survival analysis, researchers should be careful about data quality. In this sense, imputation techniques are used to correctly estimate and complete censored data points. This study introduces the kernel smoothing method as an imputation method that takes into account the structure of the data and the individual effects of the accessible data points with kernel weights. The basic idea is to obtain a nonparametric model from the missing data set and consider sample predictions to estimate the censored ones. A simulation study is conducted to show the benefits of the method, and it is also compared with Ordinary Least Squares (OLS) based imputation, which is one of the widely used imputation methods and works similar to the proposed method.
Keywords
Kernel smoothing, right-censored data, imputation technique, OLS imputation
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