Inference of progressively type-II censored competing risks data from Chen distribution with an application

Ahmed, Essam A.; Alhussain, Ziyad A.; Salah, Mukhtar M.; Ahmed, Hanan; Eliwa, Mohamed S.

doi:10.1080/02664763.2020.1815670

Cited by 23 publications

(13 citation statements)

References 42 publications

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“…To illustrate the relevance of the offered inference procedures to a real phenomenon, we shall apply the real-life test data set reported by Doganaksoy et al (2002). Recently, this dataset has also been investigated by Ahmed et al (2020) and Ren and Gui (2021a).…”

Section: Electrodes Data Analysismentioning

confidence: 99%

Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models

Elshahhat

Nassar

2023

Stat Papers

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Recently, an improved adaptive Type-II progressive censoring scheme is proposed to ensure that the experimental time will not pass a pre-fixed time and ends the test after recording a pre-fixed number of failures. This paper studies the inference of the competing risks model from Weibull distribution under the improved adaptive progressive Type-II censoring. For this goal, we used the latent failure time model with Weibull lifetime distributions with common shape parameters. The point and interval estimation problems of parameters, reliability and hazard rate functions using the maximum likelihood and Bayesian estimation methods are considered. Moreover, making use of the asymptotic normality of classical estimators and delta method, approximate intervals are constructed via the observed Fisher information matrix. Following the assumption of independent gamma priors, the Bayes estimates of the scale parameters have closed expressions, but when the common shape parameter is unknown, the Bayes estimates cannot be formed explicitly. To solve this difficulty, we recommend using Markov chain Monte Carlo routine to compute the Bayes estimates and to construct credible intervals. A comprehensive Monte Carlo simulation is conducted to judge the behavior of the offered methods. Ultimately, analysis of electrodes data from the life-test of high-stress voltage endurance is provided to illustrate all proposed inferential procedures.

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Section: Electrodes Data Analysismentioning

confidence: 99%

Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models

Elshahhat

Nassar

2023

Stat Papers

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“…Such a model is Type-II censored competing risk model with two competing risk factors. It is studied in [1]. The authors propose EM-algorithm to compute the solution of the Maximum Likelihood system of equations for estimation of the parameters of Chen's distribution.…”

Section: Dependent and Censored Datamentioning

confidence: 99%

Editorial to special issue V WCDANM 2018

Stehlík

Grilo

Jordanova

2020

Journal of Applied Statistics

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Branching processesBulgarian probabilistic school has long traditions in studying branching processes. Tchorbadjieff and Mayster [36] start with a very good summary of the contemporary results in this area. The authors study a linear birth-death process starting with a random number of particles. At first, they consider the cases when the initial distribution follows different standard probabilistic distributions -Negative Binomial and its closest Geometric, Poisson or Pólya-Aeppli distributions. They proved analytically and numerically that the random initial conditions can not change the critical parameter of branching mechanism, but they CONTACT M.

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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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Inference of progressively type-II censored competing risks data from Chen distribution with an application

Abstract: Appendix 1: The proof of the Theorem 1.(I) Suppose that g 0 ( ) " < 1 and that 1 and 2 are both …xed points in (0; 1). If 1 6 = 2 , then the Mean Value Theorem implies that a number k exists between 1 and 2 , and hence

Cited by 23 publications

References 42 publications

Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models

Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models

Editorial to special issue V WCDANM 2018

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

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