Partially Accelerated Life Tests for the Weibull Distribution Under Multiply Censored Data

Wang, Fu‐Kwun; Cheng, Yijun; Lu, Wanbo

doi:10.1080/03610918.2011.615434

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…To obtain reliable initial estimates for iterative procedures they also proposed application of censored estimation technique with one-step bias correction. Wang et al (2012) presented a step-stress partially accelerated life tests to estimate the parameters of Weibull distribution which considered multiply censored data.…”

Section: Censored Datamentioning

confidence: 99%

Numerical Methods for Reliability and Safety Assessment

Kadry¹,

Hami²

2015

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Section: Censored Datamentioning

confidence: 99%

Numerical Methods for Reliability and Safety Assessment

Kadry¹,

Hami²

2015

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“…So far, numerous researchers have investigated progressive censored data under SSPALT, but there has been relatively little work on multiply censored data. In SSPALT, Wang et al 21 used multiply censored data to produce MLEs of the parameters of the Weibull distribution and the AF. Jia et al 22 estimated the reliability using MLEs and Bayes parameter estimates and investigated how to generate confidence intervals for reliability under a multiple censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data

Rahman¹,

Kamal²,

Khan³

et al. 2023

Sci Rep

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Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly important in life testing studies. In this article, we concentrate on parametric inference for step stress partially life testing utilizing multiple censored data based on the Tampered Random Variable model. Under normal stress circumstances, the lifespan of the experimental units is assumed to follow the Nadarajah–Haghighi distribution, with and being the shape and scale parameters, respectively. Maximum likelihood estimates for model parameters and acceleration factor are developed using multiple censored data. We build asymptotic confidence intervals for the unknown parameters using the observed Fisher information matrix. To demonstrate the applicability of the different methodologies, an actual data set based on the timings of subsequent failures of consecutive air conditioning system failures for each member of a Boeing 720 jet aircraft fleet is investigated. Finally, thorough simulation studies utilizing various censoring strategies are performed to evaluate the estimate procedure performance. Several sample sizes were studied in order to investigate the finite sample features of the considered estimators. According to our numerical findings, the values of mean squared errors and average asymptotic confidence intervals lengths drop as sample size increases. Furthermore, when the censoring level is reduced, the considered estimates of the parameters approach their genuine values.

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“…Also, the confidence intervals of the estimators are obtained. Wang et al [8] studied the estimation of the parameters of the Weibull distribution in step-stress ALT under multiply censored data. The MLEs are used to obtain the parameters of the Weibull distribution and the acceleration factor under multiply censored data.…”

Section: Introductionmentioning

confidence: 99%

Parametric Analysis of Gumbel Type-II Distribution under Step-stress Life Test

Dutta¹,

Sultana²,

Kayal³

2021

Preprint

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In this paper, we focus on the parametric inference based on the Tampered Random Variable (TRV) model for simple step-stress life testing (SSLT) using Type-II censored data. The baseline lifetime of the experimental units under normal stress conditions follows Gumbel Type-II distribution with α and λ being the shape and scale parameters, respectively. Maximum likelihood estimator (MLE) and Bayes estimator of the model parameters are derived based on Type-II censored samples. We obtain asymptotic intervals of the unknown parameters using the observed Fisher information matrix. Bayes estimators are obtained using Markov Chain Monte Carlo (MCMC) method under squared error loss function and LINEX loss function. We also construct highest posterior density (HPD) intervals of the unknown model parameters.

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Partially Accelerated Life Tests for the Weibull Distribution Under Multiply Censored Data

Cited by 13 publications

References 8 publications

Numerical Methods for Reliability and Safety Assessment

Numerical Methods for Reliability and Safety Assessment

Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data

Parametric Analysis of Gumbel Type-II Distribution under Step-stress Life Test

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