Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Shi, Xiaolin; Shi, Yimin

doi:10.3390/e23091099

Cited by 14 publications

(2 citation statements)

References 31 publications

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“…For this reason, Wu and Kuş [8] introduced life testing, which combines first-failure censoring with progressive type-II censoring, namely as a progressive first-failure censoring (PFFC) scheme. Several authors have discussed inference under a PFFC scheme for different lifetime distributions; see, for example, Haj et al [9], Abushal [10], Soliman et al [11,12], Mahmoud et al [13], Ahmed [14], Xie and Gui [15] and Shi and Shi [16]. This censoring scheme has advantages in terms of reducing test time in which more items are used but only m of n × k items are failures.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model

et al. 2022

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This paper deals with the estimation of the parameters for asymmetric distribution and some lifetime indices such as reliability and hazard rate functions based on progressive first-failure censoring. Maximum likelihood, bootstrap and Bayesian approaches of the distribution parameters and reliability characteristics are investigated. Furthermore, the approximate confidence intervals and highest posterior density credible intervals of the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators and Markov chain Monte Carlo technique, respectively. In addition, the delta method is implemented to obtain the variances of the reliability and hazard functions. Moreover, we apply two methods of bootstrap to construct the confidence intervals. The Bayes inference based on the squared error and LINEX loss functions is obtained. Extensive simulation studies are conducted to evaluate the behavior of the proposed methods. Finally, a real data set of the COVID-19 mortality rate is analyzed to illustrate the estimation methods developed here.

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Section: Introductionmentioning

confidence: 99%

Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model

et al. 2022

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“…Ashour et al [7] studied both Bayesian and non-Bayesian estimations for the Weibull parameters using binomial random removals. Shi and Shi [8] considered an inference for the inverse power Lomax distribution. See also, for further information, the works of Xie and Gui [9], Saini et al [10] and Cai and Gui [11], among others.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Alpha Power Exponential Parameters Using Progressive First-Failure Censoring with Applications

Nassar

Alotaibi

Elshahhat

2022

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This paper is an endeavor to investigate some estimation problems of the unknown parameters and some reliability measures of the alpha power exponential distribution in the presence of progressive first-failure censored data. In this regard, the classical and Bayesian approaches are considered to acquire the point and interval estimates of the different quantities. The maximum likelihood approach is proposed to obtain the estimates of the unknown parameters, reliability, and hazard rate functions. The approximate confidence intervals are also considered. The Bayes estimates are obtained by considering both symmetric and asymmetric loss functions. The Bayes estimates and the associated highest posterior density credible intervals are given by applying the Monte Carlo Markov Chain technique. Due to the complexity of the given estimators which cannot be compared theoretically, a simulation study is implemented to compare the performance of the different procedures. In addition, diverse optimality criteria are employed to pick the best progressive censoring plans. Two engineering applications are considered to illustrate the applicability of the offered estimators. The numerical outcomes showed that the Bayes estimates based on symmetric or asymmetric loss functions perform better than other estimates in terms of minimum root mean square errors and interval lengths.

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Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples

Saini

Garg

2022

Comput Stat

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Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Cited by 14 publications

References 31 publications

Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model

Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model

Statistical Analysis of Alpha Power Exponential Parameters Using Progressive First-Failure Censoring with Applications

Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples

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