Table of Contents Author Guidelines Submit a Manuscript
Journal of Probability and Statistics
Volume 2013 (2013), Article ID 146140, 16 pages
http://dx.doi.org/10.1155/2013/146140
Research Article

Bayesian Estimation and Prediction for Flexible Weibull Model under Type-II Censoring Scheme

Department of Statistics and DST-CIMS, Banaras Hindu University, Varanasi 221005, India

Received 4 April 2013; Revised 3 June 2013; Accepted 18 June 2013

Academic Editor: Shein-chung Chow

Copyright © 2013 Sanjay Kumar Singh et al. 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.

Linked References

  1. I. R. Dunsmore, “The Bayesian predictive distribution in life testing models,” Technometrics, vol. 16, pp. 455–460, 1974. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  2. E. K. AL-Hussaini, “Predicting observables from a general class of distributions,” Journal of Statistical Planning and Inference, vol. 79, no. 1, pp. 79–91, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  3. E. K. Al-Hussaini, “On Bayes prediction of future median,” Communications in Statistics, vol. 30, no. 7, pp. 1395–1410, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. B. Pradhan and D. Kundu, “Bayes estimation and prediction of the two-parameter gamma distribution,” Journal of Statistical Computation and Simulation, vol. 81, no. 9, pp. 1187–1198, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  5. D. Kundu and M. Z. Raqab, “Bayesian inference and prediction of order statistics for a Type-II censored Weibull distribution,” Journal of Statistical Planning and Inference, vol. 142, no. 1, pp. 41–47, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  6. D. Kundu and H. Howlader, “Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data,” Computational Statistics & Data Analysis, vol. 54, no. 6, pp. 1547–1558, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  7. I. G. Evans and A. M. Nigm, “Bayesian prediction for two-parameter Weibull lifetime models,” Communications in Statistics, vol. 9, no. 6, pp. 649–658, 1980. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  8. S. K. Singh, U. Singh, and V. K. Sharma, “Bayesian estimation and prediction for the generalized lindley distribution under assymetric loss function,” Hacettepe Journal of Mathematics and Statistics. In press. View at Google Scholar
  9. S. K. Singh, U. Singh, and V. K. Sharma, “Bayesian prediction of future observations from inverse weibull distribution based on type-ii hybrid censored sample,” International Journal of Advanced Statistics and Probability, vol. 1, pp. 32–43, 2013. View at Google Scholar
  10. M. Bebbington, C.-D. Lai, and R. Zitikis, “A flexible weibull extension,” Reliability Engineering and System Safety, vol. 92, no. 6, pp. 719–726, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. A. F. M. Smith and G. O. Roberts, “Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods,” Journal of the Royal Statistical Society B, vol. 55, no. 1, pp. 3–23, 1993. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. W. K. Hastings, “Monte carlo sampling methods using Markov chains and their applications,” Biometrika, vol. 57, no. 1, pp. 97–109, 1970. View at Publisher · View at Google Scholar · View at Scopus
  13. S. P. Brooks, “Markov chain Monte Carlo method and its application,” Journal of the Royal Statistical Society D, vol. 47, no. 1, pp. 69–100, 1998. View at Google Scholar · View at Scopus
  14. B. Efron and R. Tibshirani, “Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy,” Statistical Science, vol. 1, no. 1, pp. 54–77, 1986. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. M.-H. Chen and Q.-M. Shao, “Monte Carlo estimation of Bayesian credible and HPD intervals,” Journal of Computational and Graphical Statistics, vol. 8, no. 1, pp. 69–92, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  16. H. A. David and H. N. Nagaraja, Order Statistics, Wiley Series in Probability and Statistics, Wiley, New York, NY, USA, 3rd edition, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  17. J. O. Berger, Statistical Decision Theory and Bayesian Analysis, Springer Series in Statistics, Springer, New York, NY, USA, 2nd edition, 1985. View at MathSciNet
  18. R Development Core Team, “R: a language and environment for statistical computing,” Tech. Rep., R Foundation for Statistical Computing, Vienna, Austria, 2012, http://www.r-project.org/. View at Google Scholar
  19. M. S. Suprawhardana, Prayoto, and Sangadji, “Total time on test plot analysis for machenical components of the rsg-gas reactor,” Atom Inones, vol. 25, no. 2, 1999. View at Google Scholar