Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring

Goyal, Teena; Rai, Piyush K; Maurya, S. K.

doi:10.1007/s40745-020-00263-3

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…For example, in Type-II censoring, the experiment termination time is uncontrolled, whereas the efficiency level is controlled in Type-I censoring. For more details and some recent references on CSs, see [26][27][28][29][30][31][32][33][34][35][36].…”

Section: Plos Onementioning

confidence: 99%

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

2022

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In this article, we investigate the problem of point and interval estimations under constant-stress partially accelerated life tests. The lifetime of items under use condition is assumed to follow the two-parameter inverted Kumaraswamy distribution. Based on Type-I progressively hybrid censored samples, the maximum likelihood and Bayesian methods are applied to estimate the model parameters as well as the acceleration factor. Under linear exponential, general entropy and squared error loss functions, Bayesian method outcomes are obtained. In addition, interval estimation is achieved by finding approximately confidence intervals for the parameters, as well as credible intervals. To investigate the accuracy of the obtained estimates and to compare the performance of confidence intervals, a Monte Carlo simulation is developed. Finally, a set of real data is analyzed to demonstrate the estimation procedures.

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Section: Plos Onementioning

confidence: 99%

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

2022

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“…Under Type-I PHCS, life tests terminate either after a predetermined number of failures or when the specified duration of the experiment has elapsed. For further information and recent references on progressive hybrid censoring, please refer to [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Empirical Bayes Estimation to Assess Reliability in Inverted Exponentiated Rayleigh Distribution with Progressive Hybrid Censored Medical Data

Hashem,

Alyami,

Yousef

2023

Axioms

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This study addresses the issue of estimating the shape parameter of the inverted exponentiated Rayleigh distribution, along with the assessment of reliability and failure rate, by utilizing Type-I progressive hybrid censored data. The study explores the estimators based on maximum likelihood, Bayes, and empirical Bayes methodologies. Additionally, the study focuses on the development of Bayes and empirical Bayes estimators with balanced loss functions. A concrete example based on actual data from the field of medicine is used to illustrate the theoretical insights provided in this study. Monte Carlo simulations are employed to conduct numerical comparisons and evaluate the performance and accuracy of the estimation methods.

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“…Reference [18] discussed adaptive Type-II progressive censoring schemes of maximum product spacing for Weibull parameters. Reference [19] discussed classical and Bayesian inferences for the generalized DUS exponential distribution under Type-I progressive hybrid censored data.…”

Section: Introductionmentioning

confidence: 99%

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

Alshenawy¹,

Sabry²,

Almetwally³

et al. 2021

Computers, Materials &Amp; Continua

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Maximum product spacing for stress-strength model based on progressive Type-II hybrid censored samples with different cases has been obtained. This paper deals with estimation of the stress strength reliability model R = P(Y < X) when the stress and strength are two independent exponentiated Gumbel distribution random variables with different shape parameters but having the same scale parameter. The stress-strength reliability model is estimated under progressive Type-II hybrid censoring samples. Two progressive Type-II hybrid censoring schemes were used, Case I: A sample size of stress is the equal sample size of strength, and same time of hybrid censoring, the product of spacing function under progressive Type-II hybrid censoring schemes. Case II: The sample size of stress is a different sample size of strength, in which the life-testing experiment with a progressive censoring scheme is terminated at a random time T 2 0; 1 ð Þ. The maximum likelihood estimation and maximum product spacing estimation methods under progressive Type-II hybrid censored samples for the stress strength model have been discussed. A comparison study with classical methods as the maximum likelihood estimation method is discussed. Furthermore, to compare the performance of various cases, Markov chain Monte Carlo simulation is conducted by using iterative procedures as Newton Raphson or conjugate-gradient procedures. Finally, two real datasets are analyzed for illustrative purposes, first data for the breaking strengths of jute fiber, and the second data for the waiting times before the service of the customers of two banks.

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Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring

Cited by 10 publications

References 27 publications

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

Utilizing Empirical Bayes Estimation to Assess Reliability in Inverted Exponentiated Rayleigh Distribution with Progressive Hybrid Censored Medical Data

Product Spacing of Stress-Strength Under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution

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