A Full Bayesian Approach for Masked Data in Step-Stress Accelerated Life Testing

Xu, Ancha; Basu, Sanjib; Tang, Yincai

doi:10.1109/tr.2014.2315940

Cited by 49 publications

(23 citation statements)

References 23 publications

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“…For this, we shall adopt the log-link model, considering the normal prior (see Section 4) and the log-gamma priors used by Sha and Pan. 16 Thus, additional to (17), we shall apply the priors g ( ) ∝ e − e , = 0, 1, with = = 0.01, corresponding to vague prior information. In case the common shape parameter is unknown, following the work of Sha and Pan, 16 the corresponding prior is a truncated log-gamma, ie, we consider = log(X) where X ∼ truncated Gamma( , ) with X > 1.…”

Section: A Real Examplementioning

confidence: 99%

“…They compare their approach to the classical ML estimation in terms of characteristic examples. Furthermore, Xu et al propose a Bayesian analysis of SSALT experiments for series system failure data when the exact failure cause is not identified, but the set of all potential risks is known.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Lee and Pan 12,13 work out the model for exponential type-II censored lifetimes applying joint conjugate priors and providing practical guidelines for eliciting expert's knowledge. The exponential type-I censored case is dealt within the work of Van Dorp et al 14 17 propose a Bayesian analysis of SSALT experiments for series system failure data when the exact failure cause is not identified, but the set of all potential risks is known.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Kohl

Kateri

2019

Appl Stoch Models Bus & Ind

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A step‐stress accelerated life testing model is considered for progressive type‐I censored experiments when the tested items are not monitored continuously but inspected at prespecified time points, producing thus grouped data. The underlying lifetime distributions belong to a general scale family of distributions. The points of stress‐level change are simultaneously inspection points as well while there is the option of assigning additional inspection points in between the stress‐level change points. In a Bayesian framework, the posterior distributions of the parameters of the model are derived for characteristic choices of prior distributions, as conjugate‐like and normal priors; vague or noninformative. The developed approach is illustrated on a simulated example and on a real data set, both known from the literature. The results are compared to previous analyses; frequentist or Bayes.

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Section: A Real Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Kohl

Kateri

2019

Appl Stoch Models Bus & Ind

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“…Yang et al discussed the statistical analysis of incomplete data in the PMU system. Xu et al presented a full Bayesian method to analysis the masked data in step‐stress accelerated life testing (SSALT). Hsu et al proposed the Bayesian analysis of SSALT model under the assumption that the masking probability depends on the component.…”

Section: Introductionmentioning

confidence: 99%

E‐Bayesian and hierarchical Bayesian estimations for parallel system model in the presence of masked data

Cai

Shi

Lin

2019

Concurrency and Computation

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Summary In this paper, we consider the statistical analysis of parallel system with inverse Weibull distributed components. Due to cost and time constraints, the causes of system failures are masked and the type‐II censored observations might occur in the collected data. Under the symmetric and asymmetric loss functions, the expected Bayesian (E‐Bayesian) method and the hierarchical Bayesian method are proposed to estimate the parameters, as well as the reliability function. Numerical simulations using the Monte Carlo (MC) method are given to demonstrate the performances of the estimations under different masking levels and effective sample sizes. Finally, one data set is analyzed for illustrative purpose.

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“…Xu, Tang, and Guan () applied Bayesian analysis to masked data in step‐stress accelerated lifetime tests (SSALT). Xu, Basu, and Tang () proposed a fully Bayesian method for analyzing masked data in SSALT. Fan, Hsu, and Peng () provided Bayesian inference for masked systems in SSALT.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring

Cai

Shi²,

Liu³

2017

Naval Research Logistics

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This paper considers the statistical analysis of masked data in a series system, where the components are assumed to have Marshall‐Olkin Weibull distribution. Based on type‐I progressive hybrid censored and masked data, we derive the maximum likelihood estimates, approximate confidence intervals, and bootstrap confidence intervals of unknown parameters. As the maximum likelihood estimate does not exist for small sample size, Gibbs sampling is used to obtain the Bayesian estimates and Monte Carlo method is employed to construct the credible intervals based on Jefferys prior with partial information. Numerical simulations are performed to compare the performances of the proposed methods and one data set is analyzed.

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A Full Bayesian Approach for Masked Data in Step-Stress Accelerated Life Testing

Cited by 49 publications

References 23 publications

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

E‐Bayesian and hierarchical Bayesian estimations for parallel system model in the presence of masked data

Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring

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