On Estimation of Stress-Strength Reliability Using Lower Record Values from Proportional Reversed Hazard Family

Q3 Business, Management and Accounting American Journal of Mathematical and Management Sciences Pub Date : 2020-02-17 DOI:10.1080/01966324.2020.1722299
Ajit Chaturvedi, Ananya Malhotra
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引用次数: 5

Abstract

Abstract In this article, a study on the stress-strength parameter based on lower record values from one parameter proportional reversed hazard family (PRHF) has been conducted. The classical and Bayesian results of Khan and Arshad (UMVU Estimation of Reliability Function and Stress-Strength Reliability from Proportional Reversed Hazard Family Based on Lower Records. American Journal of Mathematical and Management Sciences, 35(2), 171–181) and Condino et al. (Likelihood and Bayesian estimation of P(Y < X) using lower record values from a general class of distributions. Statistical Papers), when the strength and stress variables belong to different family of distributions from PRHF have been generalized. Uniformly minimum variance unbiased estimator (UMVUE), maximum likelihood estimator (MLE) and Bayes estimator (BE) are obtained for the powers of the parameter and reliability functions and The estimators of three parametric functions, namely, powers of parameter, and are interrelated, whereas, in the literature, researchers have handled the three estimation problems separately. Moreover, it is has been shown that the expressions for and are not required to estimate them. In this article, the technique of obtaining estimators of and is simpler as it does not require Rao-Blackwellization. Simulation studies have been performed for analyzing the behavior of the proposed estimators. An example using real data has also been considered as an illustration.
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利用比例反向危险族的低记录值估算应力强度可靠性
摘要本文对基于单参数比例反向危险族(PRHF)较低记录值的应力强度参数进行了研究。Khan和Arshad的经典和贝叶斯结果(基于较低记录的比例反向危险族的可靠性函数和应力强度可靠性的UMVU估计。美国数学与管理科学杂志,35(2),171-181)和Condino等人(P(Y)的似然和贝叶斯估计 < X) 使用来自一般分布类别的较低记录值。统计学论文),当强度和应力变量属于不同的分布族时,PRHF已经被推广。获得了参数和可靠性函数的幂的一致最小方差无偏估计量(UMVUE)、最大似然估计量(MLE)和贝叶斯估计量(BE)。三个参数函数(即参数的幂)的估计量是相互关联的,而在文献中,研究人员分别处理了这三个估计问题。此外,已经表明,不需要和的表达式来估计它们。在本文中,获得和的估计量的技术更简单,因为它不需要Rao-Blackellization。已经进行了模拟研究来分析所提出的估计器的行为。一个使用真实数据的例子也被认为是一个例证。
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来源期刊
American Journal of Mathematical and Management Sciences
American Journal of Mathematical and Management Sciences Business, Management and Accounting-Business, Management and Accounting (all)
CiteScore
2.70
自引率
0.00%
发文量
5
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