Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring

Nik, Ali Saadati; Asgharzadeh, A.; Raqab, Mohammad Z.

doi:10.1016/j.matcom.2021.06.005

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…Some scholars have conducted valuable research for the NP distribution in the literature, such as [10,11,15]. However, Bayesian methods may encounter challenges with high-dimensional, complex models, and big data in practice.…”

Section: Discussionmentioning

confidence: 99%

“…(3) Generate a random number u from the uniform distribution U(0, 1), and then obtain α k+1 according to the following conditions (11):…”

Section: Complete Samples Casementioning

confidence: 99%

“…Take two reliability datasets from Bourguignon [6]. The first dataset (DataSet I), given by Linhart and Zucchini [18], represents the number of failures of an aircraft's air conditioning system: 23, 261, 87, 7,120,14,62,47,225,71,246,21,42,20,5,12,120,11,3,14,71,11,14,11,11,90,1,16,52,95. The second dataset (DataSet II) comes from Murthy et al [19], which is composed of the number of failures of 20 mechanical components with the following data: 0.067, 0.068, 0.076, 0.081, 0.084, 0.085, 0.085, 0.086, 0.089, 0.098, 0.098, 0.114, 0.115, 0.121, 0.125, 0.131, 0.149, 0.160, 0.485, 0.485.…”

Section: Real Data Analysismentioning

confidence: 99%

“…Nik et al [10] estimated the parameters of the NP distribution from two different perspectives, namely, classical and Bayesian statistics, in which the Bayesian estimation used importance sampling method. Nik et al [11] studied the parameter estimation as well as predicting the failure times of the removed units in multiple stages of the progressively censored sample coming from the NP distribution, in which a Bayesian method (i.e., Markov chain Monte Carlo, MCMC) was applied to estimate the unknown parameters involved in the model. Soliman [12] studied the estimation of the reliability function in a generalized life model.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Li,

Wei,

Zhao

2023

Mathematics

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In this paper, based on the mixed Gibbs sampling algorithm, a Bayesian estimation procedure is proposed for a new Pareto-type distribution in the case of complete and type II censored samples. Simulation studies show that the proposed method is consistently superior to the maximize likelihood estimation in the context of small samples. Also, an analysis of some real data is provided to test the Bayesian estimation.

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Section: Discussionmentioning

confidence: 99%

“…(3) Generate a random number u from the uniform distribution U(0, 1), and then obtain α k+1 according to the following conditions (11):…”

Section: Complete Samples Casementioning

confidence: 99%

Section: Real Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Li,

Wei,

Zhao

2023

Mathematics

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“…where C is a parameter-free constant. In-depth research on the PT-IIC scheme has been conducted in the literature using a variety of lifetime distributions; see EL-Sagheer [4,5], Dey et al [6], Chacko and Mohan [7], Nik et al [8], Alotaibi et al [9] and Maiti and Kayal [10]. One might read an outstanding review article by Balakrishnan [3] for additional information on the PT-IIC plan.…”

Section: Introductionmentioning

confidence: 99%

Estimations of Modified Lindley Parameters Using Progressive Type-II Censoring with Applications

Alotaibi

Nassar

Elshahhat

2023

Axioms

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This study addresses the estimation problems of the modified Lindley distribution using a progressive Type-II censoring plan. Using the maximum likelihood and maximum product of spacing and Bayesian estimation methods, the unknown parameter, reliability, and hazard rate functions are estimated. Employing the assumption of the gamma prior and a symmetric loss function, Bayes estimators are investigated when the observed data are obtained using the likelihood and product of spacing functions. Additionally, the approximate confidence intervals using both classical methods and the highest posterior density credible intervals are also discussed. To assess the different estimating strategies, a comprehensive simulation experiment that considers various sample sizes and censoring schemes is implemented. Finally, two actual data sets are examined to verify the utility of the modified Lindley distribution and the usefulness of the suggested estimators. The findings demonstrate that, in order to obtain the necessary estimators, the maximum product of the spacing method is preferred over the maximum likelihood method; whereas, when compared to the conventional techniques, the Bayesian approach using the likelihood and product of spacing functions provides more acceptable estimates.

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Robust estimation techniques for the tail index of the new Pareto-type distribution

Safari,

Masseran

2023

Empir Econ

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Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring

Cited by 9 publications

References 23 publications

Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Estimations of Modified Lindley Parameters Using Progressive Type-II Censoring with Applications

Robust estimation techniques for the tail index of the new Pareto-type distribution

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