Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring

The present paper, discusses classical and Bayesian estimation of unknown combined parameters of two different log-logistic models with common shape parameters and different scale parameters under a new type of censoring scheme known as joint type II censoring scheme. Maximum likelihood estimators are derived. Bayes estimates of parameters are proposed under different loss functions. Classical asymptotic confidence intervals along with the Bayesian credible intervals and Highest Posterior Density region are also constructed. Markov Chain Monte Carlo approximation method is used for simulating the theoretic results. Comparative assessment of the classical and the Bayes results are illustrated through a real archived dataset.

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“…Step 4: Repeat steps 2-3. N = 10000 times, in order to generate the sample observations ω (1) , ω (2) , . .…”

Section: Markov Chain Monte Carlo Approximationmentioning

confidence: 99%

Bayesian Estimation for the Two Log-Logistic Models Under Joint Type II Censoring Schemes

Pandey

Srivastava

2022

JRSS

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“…In contrast, the exact likelihood inference for two exponential populations under joint progressive censoring of type II was studied in [14], and some precise results were obtained based on the maximum likelihood estimates developed by [15]. Exact likelihood inference for two populations of two-parameter exponential distributions under type II joint censoring was studied by [16].…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Generalized Bayes Prediction Study Based on Joint Type-II Censoring

Abdel-Aty

Kayid

Alomani

2023

Axioms

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In this paper, the problem of predicting future failure times based on a jointly type-II censored sample from k exponential populations is considered. The Bayesian prediction intervals and point predictors were then obtained. Generalized Bayes is a Bayesian study based on a learning rate parameter. This study investigated the effects of the learning rate parameters on the prediction results. The loss functions of squared error, Linex, and general entropy were used as point predictors. Monte Carlo simulations were performed to show the effectiveness of the learning rate parameter in improving the results of prediction intervals and point predictors.

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“…Ref. [7] discussed the exact likelihood inference for two populations from two-parameter exponential distributions with the same location parameter and distinct scale parameters. They derived the exact distributions of the MLEs for three different parameters and their moment-generating functions under the joint Type-II censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme

Ding

Gui

2023

Mathematics

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The joint Type-II censoring scheme is a useful model when carrying out comparative lifecycle tests of units from various production lines. This article takes into account the estimation problem of the joint Type-II censoring data coming from two Gamma distributions with the same shape parameter but various scale parameters. The maximum likelihood estimators of the parameters from Gamma populations and asymptotic confidence intervals based on the observed Fisher information matrix are obtained. Bootstrap methods are also applied to construct confidence intervals. The Metropolis–Hastings algorithm is considered to draw Markov Chain Monte Carlo samples when computing Bayesian estimates as well as establishing the corresponding credible intervals. Monte Carlo simulations are adopted to compare the performance of the estimates. Finally, two real engineering datasets are analyzed.

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Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring

Cited by 12 publications

References 16 publications

Bayesian Estimation for the Two Log-Logistic Models Under Joint Type II Censoring Schemes

Bayesian Estimation for the Two Log-Logistic Models Under Joint Type II Censoring Schemes

Generalized Bayes Prediction Study Based on Joint Type-II Censoring

Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme

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