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dc.contributor.authorYang, Jingjing
Scott, David W.
dc.date.accessioned 2014-12-15T20:35:08Z
dc.date.available 2014-12-15T20:35:08Z
dc.date.issued 2013
dc.identifier.citation Yang, Jingjing and Scott, David W.. "Robust fitting of a Weibull model with optional censoring." Computational Statistics & Data Analysis, 67, (2013) Elsevier: 149-161. http://dx.doi.org/10.1016/j.csda.2013.05.009.
dc.identifier.urihttps://hdl.handle.net/1911/78751
dc.description.abstract The Weibull family is widely used to model failure data, or lifetime data, although the classical two-parameter Weibull distribution is limited to positive data and monotone failure rate. The parameters of the Weibull model are commonly obtained by maximum likelihood estimation; however, it is well-known that this estimator is not robust when dealing with contaminated data. A new robust procedure is introduced to fit a Weibull model by using L2 distance, i.e. integrated square distance, of the Weibull probability density function. The Weibull model is augmented with a weight parameter to robustly deal with contaminated data. Results comparing a maximum likelihood estimator with an L2 estimator are given in this article, based on both simulated and real data sets. It is shown that this new L2 parametric estimation method is more robust and does a better job than maximum likelihood in the newly proposed Weibull model when data are contaminated. The same preference for L2 distance criterion and the new Weibull model also happens for right-censored data with contamination.
dc.language.iso eng
dc.publisher Elsevier
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.
dc.title Robust fitting of a Weibull model with optional censoring
dc.type Journal article
dc.contributor.funder National Institutes of Health
dc.contributor.funder National Science Foundation
dc.contributor.funder Department of Interior National Business Center
dc.citation.journalTitle Computational Statistics & Data Analysis
dc.subject.keywordWeibull distribution
L2 distance
robust estimator
maximum likelihood
right-censored data
contamination
dc.citation.volumeNumber 67
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1016/j.csda.2013.05.009
dc.identifier.pmcid PMC3718081
dc.identifier.pmid 23888090
dc.identifier.grantID 2PO1CA082710 (National Institutes of Health)
dc.identifier.grantID DMS-09-07491 (National Science Foundation)
dc.identifier.grantID D11PC20061 (Department of Interior National Business Center)
dc.type.publication post-print
dc.citation.firstpage 149
dc.citation.lastpage 161


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