Please use this identifier to cite or link to this item: https://doi.org/10.1002/1099-1638(200007/08)16:4<281::AID-QRE338>3.0.CO;2-N
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dc.titleMore on the MIS-specification of the shape parameter with Weibull-to-exponential transformation
dc.contributor.authorXie, M.
dc.contributor.authorYang, Z.
dc.contributor.authorGaudoin, O.
dc.date.accessioned2014-04-25T10:04:54Z
dc.date.available2014-04-25T10:04:54Z
dc.date.issued2000-07
dc.identifier.citationXie, M., Yang, Z., Gaudoin, O. (2000-07). More on the MIS-specification of the shape parameter with Weibull-to-exponential transformation. Quality and Reliability Engineering International 16 (4) : 281-290. ScholarBank@NUS Repository. https://doi.org/10.1002/1099-1638(200007/08)16:4<281::AID-QRE338>3.0.CO;2-N
dc.identifier.issn07488017
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51860
dc.description.abstractWhen lifetimes follow Weibull distribution with known shape parameter, a simple power transformation could be used to transform the data to the case of exponential distribution, which is much easier to analyze. Usually, the shape parameter cannot be known exactly and it is important to investigate the effect of mis-specification of this parameter. In a recent article, it was suggested that the Weibull-to-exponential transformation approach should not be used as the confidence interval for the scale parameter has very poor statistical property. However, it would be of interest to study the use of Weibull-to-exponential transformation when the mean time to failure or reliability is to be estimated, which is a more common question. In this paper, the effect of mis-specification of Weibull shape parameters on these quantities is investigated. For reliability-related quantities such as mean time to failure, percentile lifetime and mission reliability, the Weibull-to-exponential transformation approach is generally acceptable. For the cases when the data are highly censored or when small tail probability is concerned, further studies are needed, but these are known to be difficult statistical problems for which there are no standard solutions.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1002/1099-1638(200007/08)16:4<281::AID-QRE338>3.0.CO;2-N
dc.description.sourcetitleQuality and Reliability Engineering International
dc.description.volume16
dc.description.issue4
dc.description.page281-290
dc.description.codenQREIE
dc.identifier.isiut000088865700005
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