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基于自适应逐步Ⅱ型混合截尾试验Burr-Ⅻ分布的统计分析 被引量:4

Statistical analysis of Burr-Ⅻ distribution under adaptive Type-Ⅱ progressively hybrid censored scheme
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摘要 本文分别基于自适应逐步Ⅱ型混合截尾试验和逐步Ⅱ型混合截尾试验,结合部分步进应力加速寿命试验,使用经典统计理论和Bayes理论讨论了Burr-Ⅻ分布下的统计分析问题.给出了Burr-Ⅻ分布在不同截尾试验下的极大似然估计和Bayes估计.利用Monte-Carlo模拟对自适应逐步Ⅱ型混合截尾试验和逐步Ⅱ型混合截尾试验下的参数估计进行模拟比较.结果表明,自适应逐步Ⅱ型混合截尾试验下的均方误差更小,区间长度更短,相对误差更小,所以自适应逐步Ⅱ型混合截尾试验更优越. Based on the adaptive Type-Ⅱ progressive hybrid censoring scheme and Type-Ⅱ progressive hybrid censoring scheme,introduced step-stress partially-accelerated life test,the statistical analysis of the Burr-XII distribution are studied by using by using classical statistical theory and Bayes theory.Firstly,the maximum likelihood estimation and Bayesian estimation of Burr-XII distribution based on different censoring experiments are studied by using the maximum likelihood method and Bayes theory.Then the Monte-Carlo simulation method is used to compare and analyze the parameter estimation simulation results of the adaptive Type-Ⅱ progressive hybrid censoring scheme and Type-Ⅱ progressive hybrid censoring scheme.Finally,the results prove that under the adaptive Type-Ⅱ progressive hybrid censoring scheme,the mean square error is smaller,the interval length is shorter,the maximum likelihood estimation,Bayesian estimation and relative error are smaller.So the adaptive Type-Ⅱ progressive hybrid censoring is more superior.
作者 鄢伟安 杨海军 周俊杰 YAN Weian;YANG Haijun;ZHOU Junjie(School of Transportation and Logistics,East China Jiaotong University,Nanchang 330013,China)
出处 《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2020年第5期1339-1349,共11页 Systems Engineering-Theory & Practice
基金 国家自然科学基金(71861011,61701409)。
关键词 自适应逐步Ⅱ型混合截尾试验 Burr-Ⅻ分布 部分步进应力加速寿命试验 BAYES估计 adaptive Type-Ⅱprogressive hybrid censoring scheme Burr-Ⅻdistribution step-stress partially-accelerated life test Bayesian estimation
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  • 1程依明.步进应力加速寿命试验的最优设计[J].应用概率统计,1994,10(1):52-61. 被引量:32
  • 2Usher J S, Hodgson T J. Maximum likelihood analysis of component reliability using masked system life data[J]. IEEE Trans. Reliab, 1988, 37(5): 550 -555.
  • 3Sarhan A M. Estimation of system components reliabilities using masked data[J]. Applied Mathematics and Computation, 2003, 136(1): 79-92.
  • 4Sarhan A M. Bayes estimations for reliability measures in geometric distribution model using masked system life test data[J]. Computational Statistics and Data Analysis, 2008, 52(4): 1821-1836.
  • 5Sarhan A M. Estimations of parameters in Pareto reliability model in the presence of masked data[J]. Reliab Engrg Syst Safety, 2003, 82(1): 75 -83.
  • 6Sarhan A M. Estimation of components reliability in parallel system using masked system life data[J]. Appl Math Comput, 2003, 138(1): 61 -75.
  • 7Wang F K, Keats J B, Zimmer W J. Maximum likelihood estimation of the Burr XII parameters with censored and uncensored data[J]. Microelectronics and Reliability, 1996, 36(3): 359- 362.
  • 8Johnson N L, Kotz S, Balakrishnan N. Continuous Univariate Distributions[M]. 2nd Ed. New York: John Wiley & Sons, 1995.
  • 9Guess F M, Usher J S, Hodgson T J. Estimating system and component reliabilities under partial information on cause of failure[J]. J Stat Plan Inference, 1991, 29(1-2): 75- 85.
  • 10峁诗松,王静龙,濮晓龙.高等数理统计[M].第2版.北京:高等教育出版社,2006.

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