Non-parametric Estimation of Survival Function from Grouped Observations Under Random Censorship
Biomedical Statistics and Informatics
Volume 1, Issue 1, December 2016, Pages: 1-12
Received: Oct. 8, 2016;
Accepted: Nov. 5, 2016;
Published: Dec. 5, 2016
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Job Isaac Mukangai, Department of Statistics and Actuarial Science, Kenyatta University, Nairobi, Kenya
Censoring is inevitable in survival analysis. The motivating factor for this article concerns the way censored subjects are incorporated in estimation of survival function for grouped data. In practice, the Actuarial estimator of a survival function may be biased due to unevenly distribution of censored subjects within intervals. This article presents a nonparametric estimation of a survival function using the adjusted Product Limit estimator based on grouped observations that are under random censorship. Simulation studies are carried out to assess the performance of the adjusted Product Limit estimator in comparison to the performance of Actuarial (life table) estimator to ascertain the one that is better and real data is used to show applicability of the method in real life. The results strongly indicate that adjusted Product Limit estimator of the survival function outperforms the Actuarial estimator.
Job Isaac Mukangai,
Non-parametric Estimation of Survival Function from Grouped Observations Under Random Censorship, Biomedical Statistics and Informatics.
Vol. 1, No. 1,
2016, pp. 1-12.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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