A Monte Carlo Simulation Comparison of Some Nonparametric Survival Functions for Incomplete Data

Ganesh B. Malla

Journal of Mathematics Research, 2022, vol. 14, issue 5, 1

Abstract: This article compares a new piecewise exponential estimator (NPEE) of a survival function for censored data with other three famous estimators Kaplan-Meier estimator (KME), Nelson estimator (NE), and an empirical Bayes type estimator (EBE) found in the literature. The NPEE, which is continuous on [0;1), retains the spirit of the KME and provides an exponential tail with a hazard rate determined by a novel nonparametric consideration while the other three estimators have limited usage because of their various shortcomings. In our simulation study, we employed absolute bias and relative eciency as measures of quality of the models. We chose three levels of censoring and two sample sizes and did comparisons at various quantiles. It is found that the NPEE, which is asymptotically equivalent to the KME, is shown to be better than the other three estimators for finite samples.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/jmr/article/download/0/0/47775/51272 (application/pdf)
https://ccsenet.org/journal/index.php/jmr/article/view/0/47775 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:jmrjnl:v:14:y:2022:i:5:p:1

Access Statistics for this article

More articles in Journal of Mathematics Research from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().