{"title":"Estimation of Stress Strength Reliability P [Y < X < Z] of Lomax Distribution under Different Sampling Scheme","authors":"Neethu Jacob, Anjana E. J.","doi":"10.9734/cjast/2023/v42i424271","DOIUrl":null,"url":null,"abstract":"Lomax distribution can be considered as the mixture of exponential and gamma distribution. This distribution is an advantageous lifetime distribution in reliability analysis. The applicability of Lomax distribution is not restricted only to the reliability field, but it has broad applications in Economics, actuarial modelling, queuing problems,biological sciences, etc. Initially, Lomax distribution was proposed by Lomax in 1954, and it is also known as Pareto Type II distribution. Many statistical methods have been developed for this distribution; for a review of Lomax Distribution, see [1] and the references. The stress strength model plays an important role in reliability analysis. The term stress strength was first introduced by [2]. In the context of reliability, R is defined as the probability that the unit strength is greater than stress, that is, R = P (X > Y ), where X is the random strength of the unit, and Y is the instant stress applied to it. Thus, estimation of R is very important in Reliability Analysis.The estimates of R discussed in the context of Lomax distribution are limited to the study of a single stress strength model with upper stress. But in real life, there are situations where we have to consider not only the upper stress Neethu and Anjana; Curr. J. Appl. Sci. Technol., vol. 42, no. 42, pp. 36-66, 2023; Article no.CJAST.107238 but also the lower stress. Accordingly, in the present paper, the estimation of stress strength model R = P (Y < X < Z) represents the situation where the strength X should be greater than stress Y and smaller than stress Z for Lomax distribution, Shrinkage maximum likelihood estimate and Quasi likelihood estimate are obtained both under complete and right censored data. We have considered the asymptotic confidence interval (CI) based on MLE and bootstrap CI for R. Monte Carlo simulation experiments were performed to compare the performance of estimates obtained.","PeriodicalId":10730,"journal":{"name":"Current Journal of Applied Science and Technology","volume":"18 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Journal of Applied Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/cjast/2023/v42i424271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Lomax distribution can be considered as the mixture of exponential and gamma distribution. This distribution is an advantageous lifetime distribution in reliability analysis. The applicability of Lomax distribution is not restricted only to the reliability field, but it has broad applications in Economics, actuarial modelling, queuing problems,biological sciences, etc. Initially, Lomax distribution was proposed by Lomax in 1954, and it is also known as Pareto Type II distribution. Many statistical methods have been developed for this distribution; for a review of Lomax Distribution, see [1] and the references. The stress strength model plays an important role in reliability analysis. The term stress strength was first introduced by [2]. In the context of reliability, R is defined as the probability that the unit strength is greater than stress, that is, R = P (X > Y ), where X is the random strength of the unit, and Y is the instant stress applied to it. Thus, estimation of R is very important in Reliability Analysis.The estimates of R discussed in the context of Lomax distribution are limited to the study of a single stress strength model with upper stress. But in real life, there are situations where we have to consider not only the upper stress Neethu and Anjana; Curr. J. Appl. Sci. Technol., vol. 42, no. 42, pp. 36-66, 2023; Article no.CJAST.107238 but also the lower stress. Accordingly, in the present paper, the estimation of stress strength model R = P (Y < X < Z) represents the situation where the strength X should be greater than stress Y and smaller than stress Z for Lomax distribution, Shrinkage maximum likelihood estimate and Quasi likelihood estimate are obtained both under complete and right censored data. We have considered the asymptotic confidence interval (CI) based on MLE and bootstrap CI for R. Monte Carlo simulation experiments were performed to compare the performance of estimates obtained.
洛马克斯分布可视为指数分布和伽马分布的混合分布。这种分布在可靠性分析中是一种有优势的寿命分布。洛马克斯分布的应用不仅限于可靠性领域,它在经济学、精算建模、排队问题、生物科学等领域也有广泛的应用。洛马克斯分布最初由洛马克斯于 1954 年提出,也被称为帕累托 II 型分布。关于洛马克斯分布的综述,请参见 [1] 和参考文献。应力强度模型在可靠性分析中发挥着重要作用。应力强度一词由 [2] 首次提出。在可靠性方面,R 被定义为单元强度大于应力的概率,即 R = P (X > Y ),其中 X 是单元的随机强度,Y 是施加在单元上的瞬时应力。因此,R 的估计在可靠性分析中非常重要。在洛马克斯分布中讨论的 R 估计仅限于研究具有上应力的单一应力强度模型。但在现实生活中,我们不仅要考虑上应力,还要考虑下应力。J. Appl.42, pp.因此,本文在估计应力强度模型 R = P (Y < X < Z) 表示强度 X 应大于应力 Y 且小于应力 Z 的洛马克斯分布时,在完全删失数据和右删失数据下都得到了收缩极大似然估计值和准似然估计值。我们考虑了基于 MLE 的渐近置信区间 (CI),以及基于 R 的引导置信区间 (CI)。