Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

Dutta, Subhankar; Kayal, Suchandan

doi:10.1080/16843703.2022.2027601

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…Many authors discussed the optimal censoring scheme; for more detail see Dube et al . (2016), Dutta and Kayal (2022), Elshahhat and Abu El Azm (2022), Pradhan and Kundu (2013), Sen et al . (2018), Sultan et al .…”

Section: Optimal Censoring Schemementioning

confidence: 99%

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Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

Irfan,

Sharma

2023

IJQRM

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PurposeA progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.Design/methodology/approachIn this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.FindingsThe Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.Originality/valueThe ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

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Section: Optimal Censoring Schemementioning

confidence: 99%

“…By maximizing the trace, the available data provide more precise estimates of survival probabilities. Many authors discussed the optimal censoring scheme; for more detail see Dube et al (2016), Dutta and Kayal (2022), Elshahhat and Abu El Azm (2022), Pradhan and Kundu (2013), Sen et al (2018), Sultan et al (2014).…”

Section: Optimal Censoring Schemementioning

confidence: 99%

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

Irfan,

Sharma

2023

IJQRM

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“…They demonstrated that the LE distribution is capable of modelling the breakdown times of insulating fluid data sets. Dutta and Kayal [5] addressed the estimation of the unknown parameters of the LE distribution using progressively Type-I hybrid censored data. Dey et al [6] estimated the process capability index derived from the LE distribution via classical and Bayesian approaches.…”

Section: Introductionmentioning

confidence: 99%

Inference and physics applications of the logistic-exponential parameters using adaptive progressively hybrid censoring

Nassar,

Alotaibi,

Elshahhat

2023

Phys. Scr.

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To save test time and costs and to improve the efficiency of the experiments, an adaptive Type-II progressively hybrid censored technique has been developed. Besides two standard methods of parameter inference, namely, likelihood and product of spacing approaches, the Bayesian method is further used in this study to explore the issue of estimating model parameters, reliability, and hazard rate functions of the logistic-exponential distribution via the adaptive Type-II progressively hybrid censored mechanism. The relevant approximate confidence intervals for unknown parameters of life are also acquired using the normal approximations of the frequentist estimators. Under the squared error loss function, Bayesian estimators are obtained using independent gamma priors. Because of the complicated form of the posterior distributions, the Bayes estimators and associated credible intervals cannot be computed analytically, but they can be examined by employing Monte Carlo Markov Chain methodologies. The actual performance of the offered estimation procedures is examined using Monte Carlo simulations. Four optimality criteria are also utilized to select the optimum censoring scheme. Two physical applications are considered to show the operability and applicability of the various methods. The numerical findings show that our proposed methodologies perform well and demonstrate that the offered estimates are satisfactory in practice.

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“…See, for example, Lin et al, 11 Lin et al, 12 Hemmati and Khorram, 13 Tomer and Panwar, 14 and Dutta and Kayal. 15 Under this censoring scheme, experimental time is prefixed. Here, the number of effective sample size is random.…”

Section: Introductionmentioning

confidence: 99%

Inference of a competing risks model with partially observed failure causes under improved adaptive type-II progressive censoring

Dutta

Kayal

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this paper, a competing risks model is analyzed based on improved adaptive type-II progressive censored sample (IAT-II PCS). Two independent competing causes of failures are considered. It is assumed that lifetimes of the competing causes of failure follow exponential distributions with different means. Maximum likelihood estimators (MLEs) for the unknown model parameters are obtained. Using asymptotic normality property of MLE, the asymptotic confidence intervals are constructed. Existence and uniqueness properties of the MLEs are studied. Further, bootstrap confidence intervals are computed. The Bayes estimators are obtained under symmetric and asymmetric loss functions with non-informative and informative priors. For informative priors, independent gamma distributions are considered. Highest posterior density (HPD) credible intervals are obtained. A Monte Carlo simulation study is carried out to compare performance of the established estimates. Furthermore, three different optimality criteria are proposed to obtain the optimal censoring plan. Finally, a real-life data set is considered for illustrative purposes.

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Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

Cited by 10 publications

References 18 publications

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

Inference and physics applications of the logistic-exponential parameters using adaptive progressively hybrid censoring

Inference of a competing risks model with partially observed failure causes under improved adaptive type-II progressive censoring

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