Bayes analysis of two-parameter Pareto distribution under progressively first-failure-censored data

WANG Liang, SHI Yi-min, SUN Tian-yu, CHANG Ping

Systems Engineering - Theory & Practice ›› 2012, Vol. 32 ›› Issue (11) : 2498-2503.

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Systems Engineering - Theory & Practice ›› 2012, Vol. 32 ›› Issue (11) : 2498-2503. DOI: 10.12011/1000-6788(2012)11-2498

Bayes analysis of two-parameter Pareto distribution under progressively first-failure-censored data

  • WANG Liang1, SHI Yi-min2, SUN Tian-yu2, CHANG Ping3
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Abstract

Based on a new lifetime testing plan called progressive first-failure-censoring scheme, the Bayes estimators of the unknown parameter and reliability performances have been obtained for two-parameter Pareto distribution under symmetric and asymmetric (Linex, GE) loss functions, respectively. For the Bayes estimation problem, a new approach is adopted for estimating the hyperparameters. In order to investigate the accuracy of estimators, an illustrative numerical example is presented by means of Monte-Carlo simulation.

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Pareto distribution / progressively first-failure-censored samples / loss function / Bayes estimation / Monte-Carlo simulations

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WANG Liang , SHI Yi-min , SUN Tian-yu , CHANG Ping. Bayes analysis of two-parameter Pareto distribution under progressively first-failure-censored data. Systems Engineering - Theory & Practice, 2012, 32(11): 2498-2503 https://doi.org/10.12011/1000-6788(2012)11-2498

References

[1] Lawless J F. Statistical Models and Methods for Lifetime Data[M]. 2nd ed. New York: Wiley, 2003.
[2] Balakrishnan N, Mi J. Existence and uniqueness of the MLEs for normal distribution based on general progressively type-II censored samples[J]. Statistics & Probability Letters, 2003, 64: 407-414.
[3] Balakrishnan N, Kannan N, Lin C T, et al. Point & interval estimation for Gaussian distribution, based on progressively type-II censored samples[J]. IEEE Transactions on Reliability, 2003, 52: 90-95.
[4] Ali Mousa M A M, Jaheen Z F. Statistical inference for the Burr model based on progressively censored data[J]. Computers and Mathematics with Applications, 2002, 43: 1441-1449.
[5] Li X C, Shi Y M, Wei J Q, et al. Empirical Bayes estimators of reliability performances using Linex loss under progressively type-II censored samples[J]. Mathematics and Computers in Simulation, 2007, 73: 320-326.
[6] 王亮,师义民. 逐步增加II型截尾下比例危险率模型的可靠性分析[J]. 数理统计与管理, 2011, 30(2): 315-321.Wang L, Shi Y M. Reliability analysis of proportional hazard rate model under progressively type-II censored samples[J]. Journal of Applied Statistics and Management, 2011, 30(2): 315-321.
[7] Balakrishnan N, Aggarwala R. Progressive Censoring: Theory, Methods, and Applications[M]. Boston: Birkhauser, 2000.
[8] Davis H T, Felstein M L. The generalized Pareto law as the model for progressively censored survival data[J]. Biometrika, 1979, 66: 299-306.
[9] Howlader H A, Hossain A M. Bayesian survival estimation of Pareto distribution of the second kind based on failure-censored data[J]. Computational Statistics & Data Analysis, 2002, 38: 301-314.
[10] Wu S J, Kus C. On estimation based on progressive first-failure-censored sampling[J]. Computational Statistics & Data Analysis, 2009, 53: 3659-3670.
[11] Soliman A A. Estimation of parameters of life from progressively censored data using Burr-XII model[J]. IEEE Transactions on Reliability, 2005, 54(1): 34-42.
[12] 李凌,师义民,李明海. 逐步增加的II型截尾下冷贮备串联系统可靠性指标的 Bayes估计[J]. 工程数学学报, 2007, 24(5): 895-901.Li L, Shi Y M, Li M H. Bayes estimators of the reliability performance for cold standby series systerm based on progressively type-II censored tests[J]. Chinese Journal of Engineering Mathematics, 2007, 24(5): 895-901.
[13] Balakrishnan N, Sandhu R A. A simple simulation algorithm for generating progressively type-II censored samples[J]. The American Statistician, 1995, 49: 229-230.
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