On estimation based on progressive first-failure-censored sampling

Wu, Shuo‐Jye; Kuş, Çoşkun

doi:10.1016/j.csda.2009.03.010

Cited by 119 publications

(83 citation statements)

References 21 publications

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“…In this section, first-failure-censoring scheme is combined with the progressive censoring scheme as in [21]. Suppose that independent groups with items within each group are put in a life test, 1 groups and the group in which the first failure is observed are randomly removed from the test as soon as the first failure (say 1: : : m n k ) has occurred, 2 groups and the group in which the second first failure is observed are randomly removed from the test when the second failure (say 2: : : m n k ) has occurred, and finally m groups and the group in which the first failure is observed are randomly removed from the test as soon as the …”

Section: A Progressive First-failure-censoring Schemementioning

confidence: 99%

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Estimation of the Unknown Parameters for the Compound Rayleigh Distribution Based on Progressive First-Failure-Censored Sampling

Abushal¹

2011

OJS

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This article considers estimation of the unknown parameters for the compound Rayleigh distribution (CRD) based on a new life test plan called a progressive first failure-censored plan introduced by Wu and Kus (2009). We consider the maximum likelihood and Bayesian inference of the unknown parameters of the model, as well as the reliability and hazard rate functions. This was done using the conjugate prior for the shape parameter, and discrete prior for the scale parameter. The Bayes estimators have been obtained relative to both symmetric (squared error) and asymmetric (LINEX and general entropy (GE) loss functions. It has been seen that the symmetric and asymmetric Bayes estimators are obtained in closed forms. Also, based on this new censoring scheme, approximate confidence intervals for the parameters of CRD are developed. A practical example using real data set was used for illustration. Finally, to assess the performance of the proposed estimators, some numerical results using Monte Carlo simulation study were reported.

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Section: A Progressive First-failure-censoring Schemementioning

confidence: 99%

“…Note that a first-failure-censoring scheme is terminated when the first failure in each set is observed. If an experimenter desires to remove some sets of test units before observing the first failures in these sets this life test plan is called a progressive first-failure-censoring scheme which recently introduced by [21].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Unknown Parameters for the Compound Rayleigh Distribution Based on Progressive First-Failure-Censored Sampling

Abushal¹

2011

OJS

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“…The rest of this paper is organized as follows. In Section 2, we describe the formulation of a progressive first--failure censoring scheme as described by (Wu, and Kuş, 2009). Estimation of the parameters is given in Section 3.…”

Section: Introductionmentioning

confidence: 99%

“…Also see (Wu, et.al 2001;Lee, et.al 2007). Recently, (Wu, and Kuş, 2009) obtained maximum likelihood estimates, exact confidence intervals and exact confidence regions for the parameters of Weibull distribution under the progressive first-failure censored sampling. Note that a first-failure censoring scheme is terminated when the first failure in each set is observed.…”

Section: Introductionmentioning

confidence: 99%

“…Note that a first-failure censoring scheme is terminated when the first failure in each set is observed. If an experimenter desires to remove some sets of test units before observing the first failures in these sets this life test plan is called a progressive first-failure censoring scheme which recently introduced by (Wu, and Kuş, 2009). (Soliman, et.al 2012a) obtained estimation from Burr type XII distribution using progressive first-failure censored data.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Estimation Using MCMC Approach Based on Progressive First-Failure Censoring from Generalized Pareto Distribution

Mahmoud¹,

Soliman²,

Abdellah³

et al. 2013

AJTAS

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Abstract:In this paper, based on a new type of censoring scheme called a progressive first-failure censored, the maximum likelihood (ML) and the Bayes estimators for the two unknown parameters of the Generalized Pareto (GP) distribution are derived. This type of censoring contains as special cases various types of censoring schemes used in the literature. A Bayesian approach using Markov Chain Monte Carlo (MCMC) method to generate from the posterior distributions and in turn computing the Bayes estimators are developed. Point estimation and confidence intervals based on maximum likelihood and bootstrap methods are also proposed. The approximate Bayes estimators have been obtained under the assumptions of informative and non-informative priors. A numerical example is provided to illustrate the proposed methods. Finally, the maximum likelihood and different Bayes estimators are compared via a Monte Carlo simulation study.

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Assessing the lifetime performance index for Kumaraswamy distribution under first‐failure progressive censoring scheme for ball bearing revolutions

El-Monsef

Hassanein

2020

Quality & Reliability Eng

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First‐failure progressive censoring schemes have potential usefulness in practice where budget constraints in place or there is a necessary for speedy test. To test the process capability, the lifetime performance index CL is widely recommended for evaluating the performance of the products lifetime. This paper is devoted to construct the maximum likelihood estimator of the lifetime performance index CL and the hypothesis testing technique for implementing CL under first‐failure progressive censoring sample from Kumaraswamy population. Finally, to illustrate the potentiality of the proposed procedure, two real engineering applications and a simulated example are given.

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On estimation based on progressive first-failure-censored sampling

Cited by 119 publications

References 21 publications

Estimation of the Unknown Parameters for the Compound Rayleigh Distribution Based on Progressive First-Failure-Censored Sampling

Estimation of the Unknown Parameters for the Compound Rayleigh Distribution Based on Progressive First-Failure-Censored Sampling

Bayesian Estimation Using MCMC Approach Based on Progressive First-Failure Censoring from Generalized Pareto Distribution

Assessing the lifetime performance index for Kumaraswamy distribution under first‐failure progressive censoring scheme for ball bearing revolutions

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