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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 329489, 18 pages
http://dx.doi.org/10.1155/2012/329489
Research Article

Bayesian Analysis of the Survival Function and Failure Rate of Weibull Distribution with Censored Data

1Institute for Mathematical Research, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Department of Mathematics, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Received 17 August 2012; Accepted 18 September 2012

Academic Editor: Carlo Cattani

Copyright © 2012 Chris Bambey Guure and Noor Akma Ibrahim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The survival function of the Weibull distribution determines the probability that a unit or an individual will survive beyond a certain specified time while the failure rate is the rate at which a randomly selected individual known to be alive at time will die at time ( ). The classical approach for estimating the survival function and the failure rate is the maximum likelihood method. In this study, we strive to determine the best method, by comparing the classical maximum likelihood against the Bayesian estimators using an informative prior and a proposed data-dependent prior known as generalised noninformative prior. The Bayesian estimation is considered under three loss functions. Due to the complexity in dealing with the integrals using the Bayesian estimator, Lindley’s approximation procedure is employed to reduce the ratio of the integrals. For the purpose of comparison, the mean squared error (MSE) and the absolute bias are obtained. This study is conducted via simulation by utilising different sample sizes. We observed from the study that the generalised prior we assumed performed better than the others under linear exponential loss function with respect to MSE and under general entropy loss function with respect to absolute bias.