Abstract
Estimation of a survival function from randomly censored data is very important in survival analysis. The Kaplan-Meier estimator is a very popular choice, and kernel smoothing is a simple way of obtaining a smooth estimator. In this paper, we propose a new smooth version of the Kaplan-Meier estimator using a Bezier curve. We show that the proposed estimator is strongly consistent. Numerical results reveal the that proposed estimator outperforms the Kaplan-Meier estimator and its kernel weighted smooth version in the sense of mean integrated square error.
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This research is supported by the Korea Research Foundation (1998-015-d00047) made in the program year of 1998.
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Kim, C., Park, B.U., Kim, W. et al. Bezier curve smoothing of the Kaplan-Meier estimator. Ann Inst Stat Math 55, 359–367 (2003). https://doi.org/10.1007/BF02530504
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DOI: https://doi.org/10.1007/BF02530504