Estimation of Reliability in Multicomponent Stress-Strength based On Inverse Rayleigh Distribution

Rao, G. Srinivasa; Kantam, R. R. L.; Rosaiah, K.; Reddy, J. Pratapa

doi:10.12785/jsap/020309

Cited by 44 publications

(25 citation statements)

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“…We compared our results with the existing work of Rao et al [27] at (α 1 , α 2 ) = (1.5, 1.5) and the results are displayed in Table 6. They show that average MSEs and average confidence lengths (ACLs) are lesser than the existing results based on Burr XII distribution.…”

Section: Comparison With Existing Distributionmentioning

confidence: 99%

“…Bhattacharyya and Johnson [11] expressed reliability in a multicomponent stress-strength model, as did Pandey and Uddin [23] with the Burr distribution. Rao and Kantam [24] estimated reliability in the multicomponent stress-strength model for a log-logistic distribution, Rao [25] for the exponential distribution, Rao et al [26] for an inverse Rayleigh distribution, and Rao et al [27] for the Burr XII distribution. Consider a system with k similar components and at least s (1 ≤ s ≤ k) components operating simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Reliability in a Multicomponent Stress–Strength System for the Exponentiated Moment-Based Exponential Distribution

et al. 2019

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A multicomponent system of k components with independent and identically distributed random strengths X 1 , X 2 , . . . X k , with each component undergoing random stress, is in working condition if and only if at least s out of k strengths exceed the subjected stress. Reliability is measured while strength and stress are obtained through a process following an exponentiated moment-based exponential distribution with different shape parameters. Reliability is gauged from the samples using maximum likelihood (ML) on the computed distributions of strength and stress. Asymptotic estimates of reliability are compared using Monte Carlo simulation. Application to forest data and to breaking strengths of jute fiber shows the usefulness of the model.

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Section: Comparison With Existing Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Reliability in a Multicomponent Stress–Strength System for the Exponentiated Moment-Based Exponential Distribution

et al. 2019

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“…The problem of estimating the stress-strength reliability Pr( > ) in the inverse Rayleigh distribution was considered by Rao et al [7] for ordinary samples. Soliman et al [8] discussed different methods of estimation for the inverse Rayleigh distribution based on lower record values.…”

Section: Journal Of Quality and Reliability Engineeringmentioning

confidence: 99%

Interval Estimation of Stress-Strength Reliability Based on Lower Record Values from Inverse Rayleigh Distribution

Tarvirdizade

Garehchobogh

2014

Journal of Quality and Reliability Engineering

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We consider the estimation of stress-strength reliability based on lower record values when and are independently but not identically inverse Rayleigh distributed random variables. The maximum likelihood, Bayes, and empirical Bayes estimators of are obtained and their properties are studied. Confidence intervals, exact and approximate, as well as the Bayesian credible sets for are obtained. A real example is presented in order to illustrate the inferences discussed in the previous sections. A simulation study is conducted to investigate and compare the performance of the intervals presented in this paper and some bootstrap intervals.

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“…In this regard mention may be made to Kotz et al (2003), Baklizi (2008aBaklizi ( , 2008b, Eryilmaz (2008aEryilmaz ( , 2008bEryilmaz ( , 2010Eryilmaz ( , 2011, Krishnamoorthy et al (2007Krishnamoorthy et al ( , 2009Krishnamoorthy et al ( , 2010, Krishnamoorthy and Lin (2010), Kundu and Raqab (2009), Rezaei et al (2010). Recently, Rao (2012Rao ( , 2013) studied the estimation of reliability in a multicomponent stress-strength model. Rinco(1983) initiated a study on the estimation of R p = Pr(Y p > max(Y 1 , Y 2 , · · · , Y p−1 )) and provided a particular estimator.…”

Section: Introductionmentioning

confidence: 99%

Estimation of R3 = Pr(Y3 > max(Y1, Y2)) for Exponential Distribution

Kundu¹

2017

JAS

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This paper deals with the estimation of the reliability function R3 = Pr(Y3 > max (Y1, Y2)), when the random variables Y1, Y2 and Y3 are independent exponential with respective unknown location parameters µ1, µ2 and µ3. Here we have derived several estimators of R3 which includes uniformly minimum variance unbiased estimator (UMVUE), Bayes estimator (BE) and generalized Bayes estimator(GBE). Moreover, we have compared these estimators based on their risks under different loss functions.

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Estimation of Reliability in Multicomponent Stress-Strength based On Inverse Rayleigh Distribution

Cited by 44 publications

References 0 publications

Estimation of Reliability in a Multicomponent Stress–Strength System for the Exponentiated Moment-Based Exponential Distribution

Estimation of Reliability in a Multicomponent Stress–Strength System for the Exponentiated Moment-Based Exponential Distribution

Interval Estimation of Stress-Strength Reliability Based on Lower Record Values from Inverse Rayleigh Distribution

Estimation of<i> R</i><sub><i>3</i></sub> = Pr(<i>Y<sub>3</sub> </i>> max(<i>Y<sub>1</sub>, Y<sub>2</sub></i>)) for Exponential Distribution

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