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Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 12 | Page No.: 1313-1317
DOI: 10.3923/jas.2012.1313.1317
 
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Bayesian Survival and Hazard Estimate for Weibull Censored Time Distribution

Al Omari Mohammed Ahmed, Noor Akma Ibrahim, Mohd Bakri Adam and Jayanthi Arasan

Abstract:
We consider the Weibull distribution which has been extensively used in life testing and reliability studies of the strength of materials. The maximum likelihood method is the usual frequentist approach in the parameter estimate for parametric survival data. In this study, we divert from this platform and use the Bayesian paradigm instead. The Jeffreys and extension of Jeffreys prior with the squared loss function are considered in the estimation. The Bayes estimates of the survival function and hazard rate of the Weibull distribution with censored data obtained using Lindley’s approximation are then compared to its maximum likelihood counterparts. The comparison criteria is the Mean Square Error (MSE) and the performance of these three estimates are assessed using simulations considering various sample sizes, several specific values of Weibull parameters and several values of extension of Jeffreys prior. The maximum likelihood estimates of survival function and hazard rate are more efficient than their Bayesian counterparts, however, the extension of Jeffreys is better than the maximum likelihood for certain conditions.
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How to cite this article:

Al Omari Mohammed Ahmed, Noor Akma Ibrahim, Mohd Bakri Adam and Jayanthi Arasan, 2012. Bayesian Survival and Hazard Estimate for Weibull Censored Time Distribution. Journal of Applied Sciences, 12: 1313-1317.

DOI: 10.3923/jas.2012.1313.1317

URL: https://scialert.net/abstract/?doi=jas.2012.1313.1317

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