Exact Likelihood Inference for Two Exponential Populations Under Joint Progressive Type-II Censoring

Rasouli, Abbas; Balakrishnan, N.

doi:10.1080/03610920903009418

Cited by 85 publications

(52 citation statements)

References 12 publications

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“…Balakrishnan and Rasouli [2] fo cused on the exact inference when a joint Type-II censoring scheme was im plemented between the two samples. Reference [3] deve loped exact likelihood inferential issues with regard to multiple exponential populations under different joint cen soring schemes. And recently, most researchers have in terests in the joint progressive Type-II censoring scheme [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Exact inference for two exponential populations with competing risks data

Mao

Shi

Wang

2014

J. of Syst. Eng. Electron.

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In a reliability comparative test, the joint censoring model is usually adopted to evaluate the performances of units with the same facility. However, most researchers ignore the pos sibility that there is more than one factor for the failure when a test unit fails. To solve this problem, we consider a joint Type-II hybrid censoring model for the analysis of exponential competing failure data. Based on the maximum likelihood theory, we compute the maximum likelihood estimators (MLEs) of parameters and then obtain the condition ensuring MLEs existence for every unknown parameter. Then we derive the conditional exact distributions and corresponding moment properties for parameters by the moment generating function (MGF). A Monte-Carlo simulation is conducted to compare the performances of different ways. And finally, we conduct a numerical example to illustrate the proposed method.

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Section: Introductionmentioning

confidence: 99%

Exact inference for two exponential populations with competing risks data

Mao

Shi

Wang

2014

J. of Syst. Eng. Electron.

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“…In such problems, the joint censoring scheme has been suggested in the literature. As mentioned by Balakrishnan and Rasouli [3] and Rasouli and Balakrishnan [8], a joint censoring scheme is quite useful in conducting comparative lifetime test of products coming from different units within the same facility. The joint censoring scheme is of practical significance in conducting comparative life tests of products from different lines within the same facility.…”

Section: Introductionmentioning

confidence: 99%

“…Shafay et al [10] derived the Bayesian inference for the unknown parameters of two exponential populations under joint type II censoring they developed with the use of squared-error, linear-exponential and general entropy loss functions. The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan [8] for a generalization of their results to progressive type-II censoring for the parameters of two exponential populations. Balakrishnan and Feng [4] generalized Balakrishnan and Rasouli [3], Rasouli and Balakrishnan [8] and Shafay et al [10] works by considered a jointly type II censored sample arising from h independent exponential populations.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan [8] for a generalization of their results to progressive type-II censoring for the parameters of two exponential populations. Balakrishnan and Feng [4] generalized Balakrishnan and Rasouli [3], Rasouli and Balakrishnan [8] and Shafay et al [10] works by considered a jointly type II censored sample arising from h independent exponential populations. Ashour and Abo-Kasem [1] derived Bayesian and nonBayesian estimators for two generalized exponential populations under joint type II censored scheme.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation for multiple weibull populations under joint type-II censoring

Ashour

Eraki

2014

IJASP

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In this paper, we introduce the maximum likelihood estimation for k Weibull populations under joint type II censored scheme and different special cases have been obtained. The asymptotic variance covariance matrix and approximate confidence region based on the asymptotic normality of the maximum likelihood estimators have been obtained. A numerical example is considered to illustrate the proposed estimators.

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“…Shafay et al (2013) derived the Bayesian inference for the unknown parameters of two exponential populations under joint type II censoring they developed with the use of squared-error, linear-exponential and general entropy loss functions. The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan (2010) …”

Section: Introductionmentioning

confidence: 99%

Bayesian and Non–Bayesian Estimation for Two Generalized Exponential Populations Under Joint Type II Censored Scheme

Ashour¹,

Abo-Kasem²

2014

Pak.j.stat.oper.res.

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In this paper, Bayesian and non-Bayesian estimators have been obtained for two generalized exponential populations under joint type II censored scheme, which generalize results of Balakrishnan and Rasouli (2008) and Shafay et al. (2013). The maximum likelihood estimators (MLEs) of the parameters and Bayes estimators have been developed under squared error loss function as well as under LINEX loss function. Moreover, approximate confidence region are also discussed and compared with two Bootstrap confidence regions. Also the MLE and three confidence intervals for the stress-strength parameter () R P Y X  are explored. A numerical illustration for these new results is given.

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Exact Likelihood Inference for Two Exponential Populations Under Joint Progressive Type-II Censoring

Cited by 85 publications

References 12 publications

Exact inference for two exponential populations with competing risks data

Exact inference for two exponential populations with competing risks data

Parameter estimation for multiple weibull populations under joint type-II censoring

Bayesian and Non–Bayesian Estimation for Two Generalized Exponential Populations Under Joint Type II Censored Scheme

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