Estimation of the inverted exponentiated Rayleigh Distribution Based on Adaptive Type II Progressive Hybrid Censored Sample

Panahi, Hanieh; Moradi, Nasrin

doi:10.1016/j.cam.2019.112345

Cited by 55 publications

(41 citation statements)

References 25 publications

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“…Martino et al [ 26 ] established a new approach, namely by recycling the Gibbs sampler to improve the efficiency without adding any extra computational cost. Panahi and Moradi [ 27 ] developed a hybrid strategy, combining the Metropolis–Hastings [ 28 , 29 ] algorithm with the Gibbs sampler to generate samples from the respective posterior, arising from the inverted, exponentiated Rayleigh distribution. In this paper, we adopt the method proposed in [ 27 ] to generate samples from the respective posterior arising from the GB distribution.…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Panahi and Moradi [ 27 ] developed a hybrid strategy, combining the Metropolis–Hastings [ 28 , 29 ] algorithm with the Gibbs sampler to generate samples from the respective posterior, arising from the inverted, exponentiated Rayleigh distribution. In this paper, we adopt the method proposed in [ 27 ] to generate samples from the respective posterior arising from the GB distribution. From Equations (6) and (22), the joint posterior of the parameters

can be written as

…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Therefore, we use the Metropolis–Hastings method with normal proposal distribution to generate random numbers from Equation (37). We use the next algorithm (Algorithm 1), proposed in [ 27 ], to generate random numbers from Equation (34) and construct the Bayesian credible interval of

and the entropy

. Algorithm 1 The MCMC method Step 1: Choose the initial value

…”

Section: Bayesian Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme

Shi

Zhou

2021

Entropy

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Entropy measures the uncertainty associated with a random variable. It has important applications in cybernetics, probability theory, astrophysics, life sciences and other fields. Recently, many authors focused on the estimation of entropy with different life distributions. However, the estimation of entropy for the generalized Bilal (GB) distribution has not yet been involved. In this paper, we consider the estimation of the entropy and the parameters with GB distribution based on adaptive Type-II progressive hybrid censored data. Maximum likelihood estimation of the entropy and the parameters are obtained using the Newton–Raphson iteration method. Bayesian estimations under different loss functions are provided with the help of Lindley’s approximation. The approximate confidence interval and the Bayesian credible interval of the parameters and entropy are obtained by using the delta and Markov chain Monte Carlo (MCMC) methods, respectively. Monte Carlo simulation studies are carried out to observe the performances of the different point and interval estimations. Finally, a real data set has been analyzed for illustrative purposes.

show abstract

Section: Bayesian Estimationmentioning

confidence: 99%

can be written as

…”

Section: Bayesian Estimationmentioning

confidence: 99%

and the entropy

. Algorithm 1 The MCMC method Step 1: Choose the initial value

…”

Section: Bayesian Estimationmentioning

confidence: 99%

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Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme

Shi

Zhou

2021

Entropy

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show abstract

“…, S (N ) (t) and H (1) (i) and λ (i) into Eqs. (6) and (7). Discard the first M simulated varieties which is the burn-in period (M burn-in), under the square loss function, the approximate Bayes estimates of the S(t) and…”

Section: A the Metropolis-hastings Algorithm Within Gibbs Samplingmentioning

confidence: 99%

“…Mohie El-Din et al [5] discussed the maximum likelihood (ML) and Bayes inference under the AT-II-PHC sample from the generalized exponential distribution. Panahi and Moradi [6] discussed the problem of estimating parameters of the inverted exponentiated Rayleigh distribution under under the AT-II-PHC sample. Nassar and Abo-Kasem [7] considered statistical inference for the inverse Weibull distribution under the AT-II-PHC sample.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis Based on Adaptive Progressive Hybrid Censored Sample From Alpha Power Generalized Exponential Distribution

Yan

Wang

2020

IEEE Access

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For the alpha power generalized exponential model, the maximum likelihood and Bayes estimations of unknown distribution parameters are discussed under an adaptive Type-II progressive hybrid censored sample. The confidence intervals of the distribution parameters are calculated approximately, and further employ the delta method to obtain approximate interval estimations of the associated survival function and hazard function. The Bayes estimates and the credible intervals of these quantities are also obtained. The results of simulation and analysis of an actual life data set are used to evaluate the performance the proposed method and illustrate its application possibility. INDEX TERMS Adaptive progressive hybrid censoring, likelihood inference, Bayesian method, alpha power generalized exponential distribution, survival function, hazard function.

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Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data

Elshahhat

Nassar

2021

Comput Stat

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Estimation of the inverted exponentiated Rayleigh Distribution Based on Adaptive Type II Progressive Hybrid Censored Sample

Cited by 55 publications

References 25 publications

Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme

Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme

Statistical Analysis Based on Adaptive Progressive Hybrid Censored Sample From Alpha Power Generalized Exponential Distribution

Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data

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