On adaptive progressive hybrid censored Burr type III distribution: application to the nano droplet dispersion data

Panahi, Hanieh; Asadi, Saeid

doi:10.1080/16843703.2020.1806431

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…(30) e Metropolis-Hastings sampler is employed to create samples of c inside the MCMC algorithm because the conditional distribution of c in (30) is not a well-known distribution (for further details, see [21][22][23]). e MCMC algorithm (1) in [17] is used to create λ and c samples from conditional posterior distributions, which will be utilized to approximate Bayes estimates.…”

Section: Bayesian Estimations Under Gelf Another Commonly Used Asymme...mentioning

confidence: 99%

Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model

Nagy

Bakr

Alrasheedi

2022

Mathematical Problems in Engineering

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In this article, based on the generalized Type-II progressive hybrid censoring sample from the Burr Type-XII distribution, maximum likelihood and Bayesian inference are discussed. Point and interval estimates of unknown parameters, reliability, and hazard functions are developed. We employed several loss functions, such as squared error, LINEX, and general entropy, as symmetric and asymmetric loss functions and various prior distributions as informative and non-informative priors for Bayesian inference of unknown parameters. Under a generalized Type-II progressive hybrid censoring sample, we also propose a Bayesian one-sample prediction for unobserved failures. We conduct simulation study using the MCMC algorithm for the Bayesian approach based on several prior distributions. Finally, we apply the results of the theoretical research to real data.

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Section: Bayesian Estimations Under Gelf Another Commonly Used Asymme...mentioning

confidence: 99%

Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model

Nagy

Bakr

Alrasheedi

2022

Mathematical Problems in Engineering

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“…ese methods recently have a high degree of discussions. (for details, see Ahmed [33], Panahi and Asadi [34], and Abushal [35]).…”

Section: Bayes Estimationmentioning

confidence: 99%

Estimation of Some Lifetime Parameters of Flexible Reduced Logarithmic‐Inverse Lomax Distribution under Progressive Type‐II Censored Data

Buzaridah

Ramadan

El-Desouky

2022

Journal of Mathematics

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In this study, the estimation of parameters of a three-parameter flexible reduced logarithmic-inverse Lomax (FRL-IL) distribution based on progressive type-II right censored sample is studied. These methods include maximum likelihood estimations (MLEs) and Bayesian estimators. Approximate confidence intervals (ACIs) for the reliability and hazard functions are estimated based on the asymptotic distribution of maximum likelihood estimates (MLEs). In addition, two bootstrap CIs are also proposed. Bayesian estimates are obtained for symmetric and asymmetric loss functions such as squared error loss (SEL) and linear-exponential (LINEX) loss functions. The Gibbs within Metropolis-Hasting sampler procedure is applied using the Markov Chain Monte Carlo (MCMC) technique to get the Bayes estimates of the unknown parameters and their credible intervals (CRIs). Finally, a real-life dataset that represents a group of patients with bladder cancer is considered an application of the proposed methods.

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“…Elshahhat and Nassar [18] studied the Bayesian estimation for the Hjorth distribution. See also the work of Kohansal and Shoaee [19], Panahi and Asadi [20], Ahmad et al [21], Du and Gui [22], Ateya et al [23], Alotaibi et al [24,25], and Nassar et al [26]. Recently, Elshahhat and Nassar [27] extended the APT-II HC scheme to binomial random removals.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Adaptive Progressive Type-II Hybrid Censored Dagum Data with Applications

et al. 2022

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In life testing and reliability studies, obtaining whole data always takes a long time and lots of monetary and human resources. In this case, the experimenters prefer to gather data using censoring schemes that make a balance between the length of the test, the desired sample size, and the cost. Lately, an adaptive progressive type-II hybrid censoring scheme is suggested to enhance the efficiency of the statistical inference. By utilizing this scheme, this paper seeks to investigate classical and Bayesian estimations of the Dagum distribution. The maximum likelihood and Bayesian estimation methods are considered to estimate the distribution parameters and some reliability indices. The Bayesian estimation is developed under the assumption of independent gamma priors and by employing symmetric and asymmetric loss functions. Due to the tough form of the joint posterior distribution, the Markov chain Monte Carlo technique is implemented to gather samples from the full conditional distributions and in turn obtain the Bayes estimates. The approximate confidence intervals and the highest posterior density credible intervals are also obtained. The effectiveness of the various suggested methods is compared through a simulated study. The optimal progressive censoring plans are also shown, and number of optimality criteria are explored. To demonstrate the applicability of the suggested point and interval estimators, two real data sets are also examined. The outcomes of the simulation study and data analysis demonstrated that the proposed scheme is adaptable and very helpful in ending the experiment when the experimenter’s primary concern is the number of failures.

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On adaptive progressive hybrid censored Burr type III distribution: application to the nano droplet dispersion data

Cited by 29 publications

References 25 publications

Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model

Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model

Estimation of Some Lifetime Parameters of Flexible Reduced Logarithmic‐Inverse Lomax Distribution under Progressive Type‐II Censored Data

Analysis of Adaptive Progressive Type-II Hybrid Censored Dagum Data with Applications

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