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Statistica Neerlandica
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Statistica Neerlandica
Article
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zbMATH Open
Article . 2021
Data sources: zbMATH Open
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k‐step stage life testing

\(k\)-step stage life testing
Authors: Laumen, Benjamin Christopher; Cramer, Erhard;

k‐step stage life testing

Abstract

The model of stage life testing proposed in Laumen and Cramer (2019b) is extended from two to k stages. It is illustrated that this model can be seen as an extension of progressive censoring with fixed censoring times as well as of simple step stress testing. Extending the first model, the new approach allows to incorporate information from progressively censored units subject to additional life testing. On the other hand, simple step stress modeling is generalized in the sense that only parts of the units under test are put on another stress level whereas the others remain on the initial level. The model is further studied for exponential lifetimes assuming a cumulative exposure model. We obtain the exact (conditional) distribution of the maximum likelihood estimators which is applied to construct (exact) confidence intervals for the distribution parameters. Finally, we investigate the situation of Weibull distributed lifetimes on the initial stage. The study is supplemented by illustrative simulations.

Country
Germany
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Keywords

Parametric tolerance and confidence regions, Reliability and life testing, exact confidence intervals, Exact distribution theory in statistics, Estimation in survival analysis and censored data, likelihood inference, 510, progressive censoring, simple step stress testing, exponential distribution, Weibull distribution, cumulative exposure model, info:eu-repo/classification/ddc/510, stage life testing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
Green
hybrid