Reliability tests for Weibull distribution with varying shape-parameter, based on complete data

Hisada, Kazuya; Arizino, F.

doi:10.1109/tr.2002.801845

Cited by 35 publications

(13 citation statements)

References 10 publications

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“…Exponential [1][2][3][4][5][6][7][8] and Weilbull [7][8][9][10][11][12][13][14][15][16][17][18] distributions are commonly employed to describe the statistical properties of life data in RDT. Time-censoring (Type-I censoring) instead of failure-censoring (Type-II censoring) is commonly adopted in RDT because many tests are conducted under time constraints.…”

Section: Introductionmentioning

confidence: 99%

Accelerated reliability demonstration under competing failure modes

Luo

Zhang

Chen

et al. 2015

Reliability Engineering & System Safety

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Section: Introductionmentioning

confidence: 99%

Accelerated reliability demonstration under competing failure modes

Luo

Zhang

Chen

et al. 2015

Reliability Engineering & System Safety

View full text Add to dashboard Cite

“…The shape parameter estimation for the Weibull distribution is of vital importance because its shape parameter characterizes the model and the failure rate trend . In addition, some failure modes, such as initial, random, and wear‐out, are described by its shape parameter . Methods for estimating the shape parameter for the Weibull distribution have been widely discussed in the literature .…”

Section: Introductionmentioning

confidence: 99%

“…11 In addition, some failure modes, such as initial, random, and wear-out, are described by its shape parameter. 12 Methods for estimating the shape parameter for the Weibull distribution have been widely discussed in the literature. [12][13][14][15][16] In this article, we consider a CS-PALT scheme based on the Weibull distribution, combining multiple censoring with early failures.…”

Section: Introductionmentioning

confidence: 99%

“…12 Methods for estimating the shape parameter for the Weibull distribution have been widely discussed in the literature. [12][13][14][15][16] In this article, we consider a CS-PALT scheme based on the Weibull distribution, combining multiple censoring with early failures. We attempt to solve the problem of early failures occurrences in conducting parameter estimation for the Weibull distribution in a CS-PALT scheme.…”

Section: Introductionmentioning

confidence: 99%

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Robust Estimation for Weibull Distribution in Partially Accelerated Life Tests with Early Failures

Cheng

Sheu

2015

Quality & Reliability Eng

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Maximum likelihood estimation (MLE) is a frequently used method for estimating distribution parameters in constant stress partially accelerated life tests (CS-PALTs). However, using the MLE to estimate the parameters for a Weibull distribution may be problematic in CS-PALTs. First, the equation for the shape parameter estimator derived from the log-likelihood function is difficult to solve for the occurrence of nonlinear equations. Second, the sample size is typically not large in life tests. The MLE, a typical large-sample inference method, may be unsuitable. Test items unsuitable for stress conditions may become early failures, which have extremely short lifetimes. The early failures may cause parameter estimate bias. For addressing early failures in the Weibull distribution in CS-PALTs, we propose an M-estimation method based on a Weibull Probability Plot (WPP) framework, which leads a closed-form expression for the shape parameter estimator. We conducted a simulation study to compare the M-estimation method with the MLE method. The results show that, with early-failure samples, the M-estimation method performs better than the MLE does.

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“…A great deal of literature is available on Rayleigh model under different criterions, such as Sinha (1990), Bhattacharya & Tyagi (1990), Fernandez (2000, Hisada & Arizino (2002), Ali-Mousa & Al-Sagheer (2005), Wu, Chen, and Chen (2006), Kim & Han (2009), Prakash & Prasad (2010, Prakash & Singh (2013). Soliman, Amin, and Abd-El Aziz (2010) presented results on estimation and prediction of inverse Rayleigh distribution based on lower record values.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis Under Progressively Censored Rayleigh Data

Prakash

2015

J. Mod. App. Stat. Meth.

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The one-parameter Rayleigh model is considered as an underlying model for evaluating the properties of Bayes estimator under Progressive Type-II right censored data. The One-Sample Bayes prediction bound length (OSBPBL) is also measured. Based on two different asymmetric loss functions a comparative study presented for Bayes estimation. A simulation study was used to evaluate their comparative properties.

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Reliability tests for Weibull distribution with varying shape-parameter, based on complete data

Cited by 35 publications

References 10 publications

Accelerated reliability demonstration under competing failure modes

Accelerated reliability demonstration under competing failure modes

Robust Estimation for Weibull Distribution in Partially Accelerated Life Tests with Early Failures

Bayesian Analysis Under Progressively Censored Rayleigh Data

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