An asymptotic approach to progressive censoring

Hofmann, Glenn; Cramer, Erhard; Kunert, Gerd

doi:10.1016/j.jspi.2003.08.020

Cited by 27 publications

(7 citation statements)

References 12 publications

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“…[S2] Fix (n 1 , n 2 ) = (5, 15), (10,10) and (15,5) for N = 20; (n 1 , n 2 ) = (5, 35), (10, 30), (15,25) and (20, 20) for N = 40; and search for the optimal allocation with effective sample sizes (m 1 , m 2 ) and censoring locations for different optimality criteria subject to the constraint…”

Section: -Stress-level Casementioning

confidence: 99%

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Optimal experimental plan for multi-level stress testing with Weibull regression under progressive Type-II extremal censoring

Kınacı

Kuş

et al. 2016

Communications in Statistics - Simulation and Computation

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In the design of constant-stress life-testing experiments, the optimal allocation in a multi-level stress test with Type-I or Type-II censoring based on the Weibull regression model has been studied in the literature. Conventional Type-I and Type-II censoring schemes restrict our ability to observe extreme failures in the experiment and these extreme failures are important in the estimation of upper quantiles and understanding of the tail behaviors of the lifetime distribution. For this reason, we propose the use of progressive extremal censoring at each stress level, whereas the conventional Type-II censoring is a special case. The proposed experimental scheme allows some extreme failures to be observed. The maximum likelihood estimators of the model parameters, the Fisher information and asymptotic variance-covariance matrices of the maximum likelihood estimates are derived. We consider the optimal experimental planning problem by looking at four different optimality criteria. To avoid the computational burden in searching for the optimal allocation, a simple search procedure is suggested. Optimal allocation of units for 2-and 4-stress-level situations are determined numerically. The asymptotic Fisher information matrix and the asymptotic optimal allocation problem are also studied and the results are compared with optimal allocations with specified sample sizes. Finally, conclusions and some practical recommendations are provided.

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Section: -Stress-level Casementioning

confidence: 99%

“…[S2] Fix (n 1 , n 2 , n 3 , n 4 ) = (0, 10, 10, 0), (3,7,7,3), (5,5,5,5), (6,4,4,6) and (10, 0, 0, 10) for N = 20; (n 1 , n 2 , n 3 , n 4 ) = (0, 20, 20, 0), (5,15,15,5), (8,12,12,8), (10, 10, 10, 10), (15,5,5,15) and ( …”

Section: Accepted Manuscriptmentioning

confidence: 99%

Optimal experimental plan for multi-level stress testing with Weibull regression under progressive Type-II extremal censoring

Kınacı

Kuş

et al. 2016

Communications in Statistics - Simulation and Computation

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“…Progressive type II censoring, first proposed by [6], is one of the most common censoring systems in lifetime scenarios and has received significant attention in the literature. The authors of [7] showed, for example, that in many instances, type II progressive-censoring schemes, which include ordinary type II one-stage right censoring as a particular case, could greatly outperform standard type II censoring. Reference [8] provides a brief overview of the progressive type II censoring technique.…”

Section: Introductionmentioning

confidence: 99%

Classical and Bayesian Inference of a Progressive-Stress Model for the Nadarajah–Haghighi Distribution with Type II Progressive Censoring and Different Loss Functions

et al. 2022

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Accelerated life testing (ALT) is a time-saving technology used in a variety of fields to obtain failure time data for test units in a fraction of the time required to test them under normal operating conditions. This study investigated progressive-stress ALT with progressive type II filtering with the lifetime of test units following a Nadarajah–Haghighi (NH) distribution. It is assumed that the scale parameter of the distribution obeys the inverse power law. The maximum likelihood estimates and estimated confidence intervals for the model parameters were obtained first. The Metropolis–Hastings (MH) algorithm was then used to build Bayes estimators for various squared error loss functions. We also computed the highest posterior density (HPD) credible ranges for the model parameters. Monte Carlo simulations were used to compare the outcomes of the various estimation methods proposed. Finally, one data set was analyzed for validation purposes.

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“…Hofmann et al (2005), introduced an asymptotic progressive censoring model, and found optimal censoring schemes for location-scale families based on the determinant of the covariance matrix of the asymptotic best linear unbiased estimators. The procedure is illustrated numerically when the parent distributions are Weibull and normal.…”

Section: Introductionmentioning

confidence: 99%

On Optimal Designs of Some Censoring Schemes

Awad

2016

Pak.j.stat.oper.res.

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The main objective of this paper is to explore suitability of some entropy-information measures for introducing a new optimality censoring criterion and to apply it to some censoring schemes from some underlying life-time models. In addition, the paper investigates four related issues namely; the effect of the parameter of parent distribution on optimal scheme, equivalence of schemes based on Shannon and Awad sup-entropy measures, the conjecture that the optimal scheme is one stage scheme, and a conjecture by Cramer and Bagh (2011) about Shannon minimum and maximum schemes when parent distribution is reflected power. Guidelines for designing an optimal censoring plane are reported together with theoretical and numerical results and illustrations.

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An asymptotic approach to progressive censoring

Cited by 27 publications

References 12 publications

Optimal experimental plan for multi-level stress testing with Weibull regression under progressive Type-II extremal censoring

Optimal experimental plan for multi-level stress testing with Weibull regression under progressive Type-II extremal censoring

Classical and Bayesian Inference of a Progressive-Stress Model for the Nadarajah–Haghighi Distribution with Type II Progressive Censoring and Different Loss Functions

On Optimal Designs of Some Censoring Schemes

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