A- and D-optimal progressive Type-II censoring designs based on Fisher information

Dahmen, Kirsten; Burkschat, Marco; Cramer, Erhard

doi:10.1080/00949655.2011.560118

Cited by 29 publications

(3 citation statements)

References 23 publications

(40 reference statements)

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“…The A-optimality criterion uses the sum of variances of the estimated model parameters as utility function ψ, whereas the D-optimality criterion uses an overall measure of variability given by the determinant of the covariance matrix of the estimated model parameters (see Liski et al 2002). Ng et al (2004), Balakrishnan et al (2008) and Dahmen et al (2012) considered A-and D-optimal criteria for Weibull, Lomax, normal and extreme value distributions in the context of Type-II progressive censoring scheme. Instead of using traditional A-and D-optimal criteria, Kundu (2009, 2013) considered an optimality criterion based on estimated pth quantile of the distribution of lifetime T .…”

Section: Cost Minimization-based Optimum Criterionmentioning

confidence: 99%

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On optimum life-testing plans under Type-II progressive censoring scheme using variable neighborhood search algorithm

Bhattacharya

Pradhan

Dewanji

2015

TEST

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In determination of optimum Type-II progressive censoring scheme, the experimenter needs to carry out an exhaustive search within the set of all admissible censoring schemes. The existing recommendations are only applicable for small sample sizes. The implementation of exhaustive search techniques for large sample sizes is not feasible in practice. In this article, a meta-heuristic algorithm based on variable neighborhood search approach is proposed for large sample sizes. It is found that the algorithm gives exactly the same solution for small sample sizes as the solution obtained in an exhaustive search; however, for large sample sizes, it gives near-optimum solution. We have proposed a cost function-based optimum criterion, which is scale invariant for location-scale and log-location-scale families of distribution. A sensitivity analysis is also considered to study the effect of misspecification of parameter values or cost coefficients on the optimum solution.

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Section: Cost Minimization-based Optimum Criterionmentioning

confidence: 99%

“…The corresponding Fisher information matrix for vector parameter θ is given by Dahmen et al (2012) gave a direct computation formula for Fisher information matrix as…”

Section: Progressively Censored Datamentioning

confidence: 99%

On optimum life-testing plans under Type-II progressive censoring scheme using variable neighborhood search algorithm

Bhattacharya

Pradhan

Dewanji

2015

TEST

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“…[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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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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2022

Design of Experiments for Reliability Achievement

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A- and D-optimal progressive Type-II censoring designs based on Fisher information

Cited by 29 publications

References 23 publications

On optimum life-testing plans under Type-II progressive censoring scheme using variable neighborhood search algorithm

On optimum life-testing plans under Type-II progressive censoring scheme using variable neighborhood search algorithm

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

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