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IEEE Transactions on Reliability
Article . 1989 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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On estimating the mean time to failure with unknown censoring

Authors: Shanmugam, Ramalingam; Richards, Dale O.;

On estimating the mean time to failure with unknown censoring

Abstract

Sometimes, in reliability studies, neither the life of all failed units nor the number of units still functioning is known at any specific time due to problems such as administrative delays. Consequently, one might consider an estimate of the mean time to failure (MTTF) based only on known failure times of part of the units. An investigation is conducted into the bias and efficiency of such an estimator for either an exponential or a Weibull distribution. In the exponential case, exact expressions are obtained, and, for the Weibull case, a Monte Carlo simulation was used. The estimate of MTTF based on known lifetimes of failed units alone underestimates with smaller variance and higher mean squared error than does the estimate based on the total accumulated lifetime of both failed and surviving units. >

Keywords

Bayes estimator, Reliability and life testing, bias, efficiency, Point estimation, maximum likelihood estimator, exponential distribution, Weibull distribution, mean squared error, Monte Carlo simulation, mean time-to-failure

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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!
4
Average
Top 10%
Average
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