Inference and prediction of progressive Type-II censored data from Unit-Generalized Rayleigh distribution

Wu, Ke; Wang, Liang; Zuo, Xuanjia

doi:10.15672/hujms.988054

Cited by 6 publications

(4 citation statements)

References 21 publications

(19 reference statements)

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“…. For a detailed understanding and concise summary of Prog-II CS, one can refer to authoritative sources like [25][26][27][28][29][30][31].…”

Section: Classical Likelihood Estimationmentioning

confidence: 99%

Bayesian and classical inference of the process capability index under progressive type-II censoring scheme

M Hasaballah,

A Tashkandy,

Samson Balogun

et al. 2024

Phys. Scr.

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This article uses the maximum likelihood technique, the bootstrap method, and the Markov chain Monte Carlo method to estimate the process capability index (C py ) for the generalised inverted exponential distribution. These methods are all based on the progressive Type-II censoring scheme. In reliability analysis, the generalised inverted exponential distribution is a frequently used distribution, and the C py is a critical tool in statistical process control. The manuscript proposes a comparative study of the three methods for estimating C py , and their performance is evaluated using simulation studies. Furthermore, three examples of real data is examined to show all the estimation approaches. The results demonstrate that all three methods can provide accurate estimates of C py , with the Markov chain Monte Carlo method having an advantage in providing more information on the uncertainty of the estimates. The manuscript concludes that the proposed methods can be useful in practice for estimating C py for the generalised inverted exponential distribution based on progressive Type-II censoring scheme, providing an objective measure of process performance and helping organizations to optimize their production processes.

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“…. For a detailed understanding and concise summary of Prog-II CS, one can refer to authoritative sources like [25][26][27][28][29][30][31].…”

Section: Classical Likelihood Estimationmentioning

confidence: 99%

Bayesian and classical inference of the process capability index under progressive type-II censoring scheme

M Hasaballah,

A Tashkandy,

Samson Balogun

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…Aljuaid [15], Ahmed [16], Singh et al [17], Liao ang Gui [18], Abbas et al [19], Sultan et al [20], Alshenawy [21], Mukhtar [22], Wu and Gui [23] ,Almongy et al [24], Qiao and Gui [25], Wu [26], El-Morshedy et al [27], El-Sherpieny et al [28], Liang et al [29], Alshenawy et al [30], Almetwally et al [31], Muhammed and Almetwally [32]).…”

Section: αβ Rwmentioning

confidence: 99%

Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine

Akdam¹

2023

Preprint

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The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh Weibull (RW) distribution based on progres-sive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley and Tierney-Kadane's approximation method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes esti-mates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. In addition, the coverage probabilities of the parametric bootstrap estimates are calculated. Real lifetime data sets belonging to the cancer types as bladder cancer, head and neck cancer, and leukemia are used to illustrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.

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“…There are a lot of studies that refer to the parameter estimation of different distributions under PTR-II censored samples (Ali Mousa [17], Balakrishnan [18], Ali Mousa and Al-Sagheer [19], Wu et al [20], Panahi and Asadi [21], Aljuaid [22], Ahmed [23], Singh et al [24], Liao ang Gui [25], Abbas et al [26], Sultan et al [27], Alshenawy [28], Mukhtar [29], Wu and Gui [30], Almongy et al [31], Qiao and Gui [32], Wu [33], El-Morshedy et al [34], El-Sherpieny et al [35], Liang et al [36], Alshenawy et al [37], Almetwally et al [38], Muhammed and Almetwally [39], Çiftci et al [40]). Based on the above studies, the main purpose of this study is to obtain the approximate Bayes estimators under the square error loss functions and then to check them with maximum likelihood estimators (MLEs) in the aspect of the estimated risk (ER).…”

Section: Introductionmentioning

confidence: 99%

Bayes Estimation for the Rayleigh–Weibull Distribution Based on Progressive Type-II Censored Samples for Cancer Data in Medicine

Akdam

2023

Symmetry

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The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh–Weibull (RW) distribution based on progressive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley, Tierney–Kadane approximations, and also the Markov Chain Monte Carlo (MCMC) method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes estimates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. The asymptotic confidence intervals for the unknown parameters are obtained using the MLEs of parameters. In addition, the coverage probabilities the parametric bootstrap estimates are computed. Real lifetime datasets related to bladder cancer, head and neck cancer, and leukemia are used to illustrate the empirical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.

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Inference and prediction of progressive Type-II censored data from Unit-Generalized Rayleigh distribution

Cited by 6 publications

References 21 publications

Bayesian and classical inference of the process capability index under progressive type-II censoring scheme

Bayesian and classical inference of the process capability index under progressive type-II censoring scheme

Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine

Bayes Estimation for the Rayleigh–Weibull Distribution Based on Progressive Type-II Censored Samples for Cancer Data in Medicine

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