E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme

Algarni, Ali; Almarashi, Abdullah M.; Okasha, Hassan M.; Ng, Hon Keung Tony

doi:10.3390/e22060636

Cited by 25 publications

(27 citation statements)

References 18 publications

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“…Han [11] introduced the expected Bayesian (E-Bayesian) estimation method which is very simple and it's a special Bayesian method used in the area related for the life testing of products with high reliability, small sample size or censored data. Many researchers applied the E-Bayesian method to many distributions, such as, Okasha [12], Azimi et al [13], Okasha [14], Reyad and Ahmed [15], Reyad and Ahmed [16], Nasiri and Esfandyarifar [17], Reyad et al [18], EL-Sagheer [19], Shawky and Al-Aboud [20], Reyad et al [21], Reyad and Othman [22], Han [23], Okasha [24], Rabie and Li [25], Algarni et al [26], Han [27], Piriaei et al [28] and Rabie and Li [29].…”

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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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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“…The theoretical and practical value of the research was huge, considering the life-time distribution function based on the Bayesian prediction intervals [8]. There was also an investigation devoted to the Chen distribution in the e-Bayesian assessment on the basis of the censoring scheme type-I [9]. Th present research deals with model of Chen(λ, β) in the framework of two different sampling plans to obtain Bayesian intervals for prediction needed in the future studies: In the second case, the sample of type-II, identical to that in the previous case, is presented with…”

Section: Introductionmentioning

confidence: 99%

Prediction Intervals for the First and Last Point in Future Sample Having from a New Bathtub Shape Failure Rate Life Time Model in the Presence of Outliers

2021

ijaa

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“…The parameter estimation problems for different lifetime distributions, including the mixed exponential distribution and some other mixed distribution models, have been widely studied under different censoring schemes [ 15 , 16 , 17 ]. The most commonly adopted methods to obtain the estimations for mixture models are simply using the expectation maximization (EM) algorithm or Bayesian method.…”

Section: Introductionmentioning

confidence: 99%

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Zhou

Shi

2020

Entropy

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In this paper, the evidential estimation method for the parameters of the mixed exponential distribution is considered when a sample is obtained from Type-II progressively censored data. Different from the traditional statistical inference methods for censored data from mixture models, here we consider a very general form where there is some uncertain information about the sub-class labels of units. The partially specified label information, as well as the censored data are represented in a united frame by mass functions within the theory of belief functions. Following that, the evidential likelihood function is derived based on the completely observed failures and the uncertain information included in the data. Then, the optimization method using the evidential expectation maximization algorithm (E2M) is introduced. A general form of the maximal likelihood estimates (MLEs) in the sense of the evidential likelihood, named maximal evidential likelihood estimates (MELEs), can be obtained. Finally, some Monte Carlo simulations are conducted. The results show that the proposed estimation method can incorporate more information than traditional EM algorithms, and this confirms the interest in using uncertain labels for the censored data from finite mixture models.

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E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme

Cited by 25 publications

References 18 publications

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

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

Prediction Intervals for the First and Last Point in Future Sample Having from a New Bathtub Shape Failure Rate Life Time Model in the Presence of Outliers

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

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