Volume 11 - Issue 3 (7) | PP: 430 - 436
Language : English
DOI : https://doi.org/10.31559/GJEB2021.11.3.7
DOI : https://doi.org/10.31559/GJEB2021.11.3.7
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Performance of parametric Bayesian Methods for estimating the survivor function in uncensored data using Monte-Carlo simulation
| Received Date | Revised Date | Accepted Date | Publication Date |
| 28/8/2021 | 24/10/2021 | 17/11/2021 | 1/1/2022 |
Abstract
The paper aimed to investigate the performance of some parametric survivor function estimators based on Bayesian methodology with respect to bias and efficiency. A simulation was conducted based on Mote Carlo experiments with different sample sizes different (10, 30, 50, 75, 100). The bias and variance of mean square Error V(MSE) were selected as the basis of comparison. The methods of estimation used in this study are Maximum Likelihood, Bayesian with exponential as prior distribution and Bayesian with gamma as prior distribution. A Monte Carlo Simulation study showed that the Bayesian method with gamma as prior distribution was the best performance than the other methods. The study recommended that.
How To Cite This Article
Hassan , M. E. & Musa , F. E. E. (2022). Performance of parametric Bayesian Methods for estimating the survivor function in uncensored data using Monte-Carlo simulation . Global Journal of Economics and Business, 11 (3), 430-436, 10.31559/GJEB2021.11.3.7
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