Mathematics Department, Faculty of Science, A1-Azhar University, Nasr-City, Cairo, Egypt. Faculty of Science, Islamic University, Madinah, Saudi Arabia. Mathematics Department, Sohag University, Sohag, Egypt.
In this paper, based on a new type of censoring scheme called an
adaptive type-II progressive censoring scheme introduce by Ng et
al. [1], Naval Research Logistics is considered. Based on this type of
censoring the maximum likelihood estimation (MLE), Bayes estimation, and
parametric bootstrap method are used for estimating the unknown parameters.
Also, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry
out a Bayesian estimation procedure and in turn calculate the credible
intervals. Point estimation and confidence intervals based on maximum
likelihood and bootstrap method are also proposed. The approximate Bayes
estimators obtained under the assumptions of non-informative priors, are
compared with the maximum likelihood estimators. Numerical examples using real
data set are presented to illustrate the methods of inference developed here.
Finally, the maximum likelihood, bootstrap and the different
Bayes estimates are compared via a
M. Mahmoud, A. Soliman, A. Ellah and R. El-Sagheer, "Estimation of Generalized Pareto under an Adaptive Type-II Progressive Censoring," Intelligent Information Management, Vol. 5 No. 3, 2013, pp. 73-83. doi: 10.4236/iim.2013.53008.
[1] H. K. T. Ng, D. Kudu and P. S. Chan, “Statistical Analysis of Exponential Lifetimes under an Adaptive Type-II Progressive Censoring Scheme,” Naval Research Logistics, Vol. 56, No. 8, 2010, pp. 687-698. doi:10.1002/nav.20371 [2] N. Bal Krishnan, “Progressive Censoring Methodology: An Appraisal,” Test, Vol. 16, No. 2, 2007, pp. 211-296. doi:10.1007/s11749-007-0061-y [3] N. Bal Krishnan and R. Aggarwala, “Progressive Censoring: Theory, Methods, and Applications,” Birkhauser, Boston, Berlin, 2000. [4] K.S. Lomax, “Business Failure: Another Example of the Analysis of the Failure Data,” Journal of the American Statistical Association, Vol. 49, No. 268, 1954, pp. 847-852. doi:10.1080/01621459.1954.10501239 [5] M. Habibullah and M. Ahsanullah, “Estimation of Parameters of a Pareto distribution by Generalized Order Statstics,” Communication in Statistics, Theory and Methods, Vol. 29, No. 7, 2000, pp. 1597-1609. doi:10.1080/03610920008832567 [6] S. K. Upadhyay and M. Peshwani, “Choice between Weibull and Lognormal Models: A Simulation Based Bayesian Study,” Communication in Statistics, Theory and Methods, Vol. 32, No. 2, 2003, pp. 381-405. doi:10.1081/STA-120018191 [7] A. H. Abd Ellah, “Bayesian One Sample Prediction Bounds for the Lomax Distribution,” Indian Journal Pure and Applied Mathematics, Vol. 34, No. 1, 2003, pp. 101-109. [8] A. H. Abd Ellah, “Comparison of Estimates Using Record Statstics from Lomax Model: Bayesian and Non Bayesian Approaches,” Journal of Statistical Research and Training Center, Vol. 3, No. 2, 2006, pp. 139-158. [9] E. Cramer and G. Iliopoulos, “Adaptive Progressive Type-II Censoring,” Test, Vol. 19, No. 2, 2010, pp. 342-358. doi:10.1007/s11749-009-0167-5 [10] H. A. David and H. N. Nagaraja, “Order Statistics,” 3rd Edition, Wiley, New York, 2003. doi:10.1002/0471722162 [11] H. K. T. Ng and P. S. Chan, “Comments on Progressive Censoring Methodology: An Appraisal,” Test, Vol. 16, No. 2, 2007, pp. 287-289. doi:10.1007/s11749-007-0071-9 [12] B. Efron, “The Jackknife, the Bootstrap and Other Resampling Plans,” CBMS-NSF Regional Conference Seriesin, Applied Mathematics, SIAM, Philadelphia, Vol. 38, 1982. [13] C. P. Robert and G. Casella, “Monte Carlo Statistical Methods,” 2nd Edition, Springer, New York, 2004. doi:10.1007/978-1-4757-4145-2 [14] S. Rezaei, R. Tahmasbi and M. Mahmoodi, “Estimation of P[Y