Estimation of R = P[Y X] for Weighted Exponential Distribution

Makhdoom, Iman

doi:10.3923/jas.2012.1384.1389

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Makhdoom 12 presented a model for the conventional modeling of reliability for 𝑋 and 𝑌 following WE distribution with the common scale parameter. In this section, we consider the stress-strength reliability of the WE distribution with the common shape parameter under the conventional and fuzzy notions.…”

Section: Reliability For We Distribution In a Stress-strength Setupmentioning

confidence: 99%

“…Rezaei et al 7 considered the estimation of 𝑅 for generalized Pareto distribution. For more examples, see Pak et al, 8 Mutairi et al, 9 Eryilmaz, 10 Asgharzadeh et al, 11 Makhdoom, 12 Singh et al, 13 Mokhlis et al 14 and Iranmanesh et al 15 For the comprehensive review studies of the stressstrength modeling, see Bhattacharyya and Johnson 16 and Kotz et al 17 In the conventional modeling of the reliability, the event 𝑌 < 𝑋 has to be taken into account. However, most of the engineering process inherently have uncertainty that must be dealt with and represented effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Dey et al 29 considered the reliability characteristics and various properties with several estimation techniques for estimating the unknown parameters of WE distribution. In the context of stress-strength models, Makhdoom 12 obtained the stress-strength reliability using two independent WE distributed random variables with common scale parameter. In the related literature, the estimation of the reliability characteristics in a stress-strength setup is usually studied with a conventional model.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy stress‐strength reliability for weighted exponential distribution

Yazgan

Gürler

Esemen

et al. 2021

Quality & Reliability Eng

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This study considers the reliability of the stress-strength model in the presence of the fuzziness when the stress and strength variables have weighted exponential distribution with the common shape parameter. It obtains the mean remaining strength and fuzzy mean remaining strength for the weighted exponential distribution in order to calculate how long a component can live on average under the stress. The comparative simulation results with conventional and fuzzy approaches are presented to observe the effect of the parameter changes on the models using the maximum likelihood method for different sample sizes. In addition, the developed models are applied to two real data and estimation results were generated for two approaches.

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Section: Reliability For We Distribution In a Stress-strength Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy stress‐strength reliability for weighted exponential distribution

Yazgan

Gürler

Esemen

et al. 2021

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Furthermore, they showed that the WE distribution possesses some good properties and can be used as a good fit for survival time data compared to other popular distributions such as the gamma, the WB, or the GE distribution. For example, Makhdoom [11] investigated the statistical inference for reliability and stress strength for the WE distribution. Dey et al [12] investigated various properties and methods of the estimation of the WE distribution.…”

Section: Introductionmentioning

confidence: 99%

Discriminating among Generalized Exponential, Weighted Exponential and Weibull Distributions

Niu,

Tian,

Zhang

2023

Mathematics

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In this paper, we consider the problem of discriminating among three different positively skewed lifetime distributions, namely the generalized exponential distribution, the weighted exponential distribution, and the Weibull distribution. All of these distributions have been used quite effectively to analyze positively skewed lifetime data. We use the methods of the ratio of maximized likelihood, the minimum Kolmogorov distance, and the sequential probability ratio test to discriminate among these three distributions. The probability of correct selection is considered for each hypothesis based on several scenarios with Monte Carlo simulation. Real data applications are studied to illustrate the effectiveness of these proposed methods.

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Acceptance sampling plans based on truncated life tests for weighted exponential distribution

Gui

Aslam

2015

Communications in Statistics - Simulation and Computation

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Gupta & Kundu (2009) proposed a new class of weighted exponential distributions using the idea of Azzalini. In this paper, we develop an acceptance sampling plan for the weighted exponential distribution under a truncated life test. For various acceptance numbers, consumer's confidence levels and values of the ratio of the experimental time to the specified mean lifetime, the minimum sample size necessary to ensure a certain mean lifetime are obtained. The operating characteristic function values and the associated producer's risks are also presented. A numerical example is provided to illustrate the acceptance sampling plan.

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Estimation of R = P[Y X] for Weighted Exponential Distribution

Cited by 3 publications

References 4 publications

Fuzzy stress‐strength reliability for weighted exponential distribution

Fuzzy stress‐strength reliability for weighted exponential distribution

Discriminating among Generalized Exponential, Weighted Exponential and Weibull Distributions

Acceptance sampling plans based on truncated life tests for weighted exponential distribution

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