Stress–Strength Parameter Estimation Based on Type-II Hybrid Progressive Censored Samples for a Kumaraswamy Distribution

Kohansal, Akram; Nadarajah, Saraless

doi:10.1109/tr.2019.2913461

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…; Y h:M:@ f g is described. According [30], the general likelihood function under progressive Type-II hybrid censoring schemes for stressstrength model can be written as:…”

Section: Maximum Likelihoodmentioning

confidence: 99%

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

Alshenawy¹,

Sabry²,

Almetwally³

et al. 2021

Computers, Materials &Amp; Continua

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Maximum product spacing for stress-strength model based on progressive Type-II hybrid censored samples with different cases has been obtained. This paper deals with estimation of the stress strength reliability model R = P(Y < X) when the stress and strength are two independent exponentiated Gumbel distribution random variables with different shape parameters but having the same scale parameter. The stress-strength reliability model is estimated under progressive Type-II hybrid censoring samples. Two progressive Type-II hybrid censoring schemes were used, Case I: A sample size of stress is the equal sample size of strength, and same time of hybrid censoring, the product of spacing function under progressive Type-II hybrid censoring schemes. Case II: The sample size of stress is a different sample size of strength, in which the life-testing experiment with a progressive censoring scheme is terminated at a random time T 2 0; 1 ð Þ. The maximum likelihood estimation and maximum product spacing estimation methods under progressive Type-II hybrid censored samples for the stress strength model have been discussed. A comparison study with classical methods as the maximum likelihood estimation method is discussed. Furthermore, to compare the performance of various cases, Markov chain Monte Carlo simulation is conducted by using iterative procedures as Newton Raphson or conjugate-gradient procedures. Finally, two real datasets are analyzed for illustrative purposes, first data for the breaking strengths of jute fiber, and the second data for the waiting times before the service of the customers of two banks.

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“…; Y h:M:@ f g is described. According [30], the general likelihood function under progressive Type-II hybrid censoring schemes for stressstrength model can be written as:…”

Section: Maximum Likelihoodmentioning

confidence: 99%

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

Alshenawy¹,

Sabry²,

Almetwally³

et al. 2021

Computers, Materials &Amp; Continua

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“…[8] studied the estimations of parameters for this distribution under hybrid censoring; the authors in Ref. [9] ulteriorly discussed the estimations of the stress-strength parameters based on an incorporation of progressive type II and hybrid censoring schemes. Then, the authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Jiang

Gui

2021

Symmetry

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In this paper, we address the estimation of the parameters for a two-parameter Kumaraswamy distribution by using the maximum likelihood and Bayesian methods based on simple random sampling, ranked set sampling, and maximum ranked set sampling with unequal samples. The Bayes loss functions used are symmetric and asymmetric. The Metropolis-Hastings-within-Gibbs algorithm was employed to calculate the Bayes point estimates and credible intervals. We illustrate a simulation experiment to compare the implications of the proposed point estimators in sense of bias, estimated risk, and relative efficiency as well as evaluate the interval estimators in terms of average confidence interval length and coverage percentage. Finally, a real-life example and remarks are presented.

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“…Ahmadi and Ghafouri [2] obtained the reliability estimation in a multicomponent stress-strength model under generalized half-normal distribution. Kohansal and Nadarajah [11] estimated the stress-strength parameter for a Kumaraswamy distribution. Finally, Kohansal and Shoaee [13] derived Bayesian and classical estimation of reliability in a multicomponent stress-strength model.…”

Section: Introductionmentioning

confidence: 99%

A New Flexible Stress-Strength Model

Beiranvand¹,

Kazemi

Kohansal³

et al. 2021

Stat., optim. inf. comput.

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‎To introduce a flexible stress-strength model‎, ‎statistical inference of the stress-strength parameter $R=P(X<Y)$‎, ‎when stress $X$ and strength $Y$ are two independent‎ two-parametre new Weibull-Fr\'{e}chet variables‎, ‎is considered under Type II progressive censored samples‎. ‎The MLE‎, ‎AMLE‎, ‎asymptotic confidence intervals‎, ‎Bayes estimate and HPD intervals of $R$ are achieved in three different cases‎. ‎Also‎, ‎to compare the performance of three different methods‎, ‎we apply the Monte Carlo simulations and also analyze a data set for illustrative aims‎.

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Stress–Strength Parameter Estimation Based on Type-II Hybrid Progressive Censored Samples for a Kumaraswamy Distribution

Cited by 15 publications

References 23 publications

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

A New Flexible Stress-Strength Model

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