Performance of parametric Bayesian methods for estimating the survivor function in uncensored data using monte-Carlo simulation
Author
Source
Global Journal of Economics and Business
Issue
Vol. 11, Issue 3 (31 Dec. 2021), pp.429-436, 8 p.
Publisher
Refaad Center for Studies and Research
Publication Date
2021-12-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hasan, Muhammad al-Amin& Musa, Fakhir al-Din al-Hajj Ismail. 2021. Performance of parametric Bayesian methods for estimating the survivor function in uncensored data using monte-Carlo simulation. Global Journal of Economics and Business،Vol. 11, no. 3, pp.429-436.
https://search.emarefa.net/detail/BIM-1427201
Modern Language Association (MLA)
Hasan, Muhammad al-Amin& Musa, Fakhir al-Din al-Hajj Ismail. Performance of parametric Bayesian methods for estimating the survivor function in uncensored data using monte-Carlo simulation. Global Journal of Economics and Business Vol. 11, no. 3 (Dec. 2021), pp.429-436.
https://search.emarefa.net/detail/BIM-1427201
American Medical Association (AMA)
Hasan, Muhammad al-Amin& Musa, Fakhir al-Din al-Hajj Ismail. Performance of parametric Bayesian methods for estimating the survivor function in uncensored data using monte-Carlo simulation. Global Journal of Economics and Business. 2021. Vol. 11, no. 3, pp.429-436.
https://search.emarefa.net/detail/BIM-1427201
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 436
Record ID
BIM-1427201