E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples

Okasha, Hassan M.; Wang, Chuanmei; Wang, Jianhua

doi:10.1155/2020/3510673

Cited by 4 publications

(5 citation statements)

References 36 publications

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“…For more details about E-Bayesian, readers may refer to References [10][11][12][13][14][15][16][17].…”

Section: E-bayesian Estimation For η(δ)mentioning

confidence: 99%

“…Okasha and Wang [14] provided the geometric model to E-Bayesian estimation for the unknown parameters based on record statistics using different balance loss functions. Okasha et al [15] investigated E-Bayesian point and interval predictions when only outer power parameter unknown based on type-II censored with two samples from the Burr XII distribution. The aforementioned references have the common conclusion that indicates that the E-Bayes estimate method provides better estimation than the Bayes estimate method does.…”

Section: Introductionmentioning

confidence: 99%

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E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications

2021

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Given a progressively type-II censored sample, the E-Bayesian estimates, which are the expected Bayesian estimates over the joint prior distributions of the hyper-parameters in the gamma prior distribution of the unknown Weibull rate parameter, are developed for any given function of unknown rate parameter under the square error loss function. In order to study the impact from the selection of hyper-parameters for the prior, three different joint priors of the hyper-parameters are utilized to establish the theoretical properties of the E-Bayesian estimators for four functions of the rate parameter, which include an identity function (that is, a rate parameter) as well as survival, hazard rate and quantile functions. A simulation study is also conducted to compare the three E-Bayesian and a Bayesian estimate as well as the maximum likelihood estimate for each of the four functions considered. Moreover, two real data sets from a medical study and industry life test, respectively, are used for illustration. Finally, concluding remarks are addressed.

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“…For more details about E-Bayesian, readers may refer to References [10][11][12][13][14][15][16][17].…”

Section: E-bayesian Estimation For η(δ)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications

2021

Self Cite

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show abstract

“…The three E-Bayes predictors (EBPs) for the future observation ( ) , under BSEL function can be obtained by substituting (34) and ( 43)-( 45) in (46) as given below…”

Section: Balanced Squared Error Loss Functionmentioning

confidence: 99%

“…Arshad and Jamal [33] predicted future record values using Bayesian approach of the TL family of distributions. Recently, Okasha et al [34] derived the Bayesian and E-Bayesian prediction (point and interval) based on observed order statistics with two samples from two parameter Burr XII model based on Type-II censored data. Moreover, they obtained the predictors under symmetric and asymmetric loss functions assuming gamma conjugate prior density.…”

Section: Introductionmentioning

confidence: 99%

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

AL-Dayian

EL-Helbawy

AL-Sayed

et al. 2021

JAMCS

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Prediction of future events on the basis of the past and present information is a fundamental problem of statistics, arising in many contexts and producing varied solutions. The predictor can be either a point or an interval predictor. This paper focuses on predicting the future observations from the modified Topp-Leone Chen distribution based on progressive Type-II censored scheme. The two-sample prediction is applied to obtain the maximum likelihood, Bayesian and E-Bayesian prediction (point and interval) for future order statistics. The Bayesian and E-Bayesian predictors are considered based on two different loss functions, the balanced squared error loss function; as a symmetric loss function and balanced linear exponential loss function; as an asymmetric loss function. The predictors are obtained based on conjugate gamma prior and uniform hyperprior distributions. A numerical example is provided to illustrate the theoretical results and an application using real data sets are used to demonstrate how the results can be used in practice.

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“…Prediction has been applied in a variety of disciplines such as medicine, engineering, business, economic and other areas as well. Prediction for order statistics of future observables from certain distributions has been studied by several authors, such as, Valiollahi et al (2017), Faizan and Sana (2018), Arshad and Jamal (2019), Okasha et al (2020), Ahmad (2021) and AL-Dayian et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation and Prediction for Marshall-Olkin Weibull-Exponential Distribution Based on Progressive Type-II Censoring Scheme

AL-Sayed

Behairy

2023

التجارة والتمويل

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Lifetime distributions under progressive Type-II censored scheme have been attracting great interest due to their wide application in the fields of science, engineering, social sciences and medicine. Also, prediction of future events on the basis of the past and present knowledge without any doubt is one of the most important problems in statistics. In this paper, the Bayes estimators for the parameters of the Marshall-Olkin Weibull-exponential distribution are derived based on progressive Type-II censored scheme. The estimators are considered under two different loss functions, the balanced squared error loss function; as a symmetric loss function and the balanced linear exponential loss function; as an asymmetric loss function. Also, the two-sample prediction method is applied to obtain the Bayesian prediction (point and interval) for future order statistics. A numerical example is provided to illustrate the theoretical results and an application using real data set is used to demonstrate how the results can be used in practice.

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E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples

Cited by 4 publications

References 36 publications

E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications

E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

Bayesian Estimation and Prediction for Marshall-Olkin Weibull-Exponential Distribution Based on Progressive Type-II Censoring Scheme

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