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The use of a mixed Weibull model in occupational injury analysis
dc.contributor.authorChung, Min K.en_US
dc.contributor.authorWu, Shu-Chen H.en_US
dc.contributor.authorHerrin, Gary D.en_US
dc.date.accessioned2006-04-07T19:34:57Z
dc.date.available2006-04-07T19:34:57Z
dc.date.issued1986-02en_US
dc.identifier.citationChung, Min K., Wu, Shu-Chen H., Herrin, Gary D. (1986/02)."The use of a mixed Weibull model in occupational injury analysis." Journal of Occupational Accidents 7(4): 239-250. <http://hdl.handle.net/2027.42/26278>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6X2X-469PPVC-27/2/19b559406dd1ff58295f604626539e08en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/26278
dc.description.abstractChung, M.K., Wu, S-C.H. and Herrin, G.D. (1986). The use of a mixed Weibull model in occupational injury analysis. Journal of Occupational Accidents, 7: 239-250.This article describes a mixed Weibull model which is proposed as an alternative model of occupational injury analysis. This model assumes that a worker will suffer injuries during employment with a probability [gamma] (0 &lt; [gamma] &lt; 1). The conditional failure time model is defined to be a Weibull distribution. Given the model, the relationship between minor and major injury incidents is examined using the injury data of 1,004 workers in a south-western industrial plant. The efficacy of the model is apparent in that it provides a fraction of long-term survivors as well as injury rate for those ever suffering an injury.en_US
dc.format.extent934095 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleThe use of a mixed Weibull model in occupational injury analysisen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Industrial and Systems Engineering, The University of Illinois at Chicago, Chicago, IL 60680, U.S.A.en_US
dc.contributor.affiliationotherDivision of Epidemiology and Health Computer Sciences, The University of Minnesota, Minneapolis, MN 55455, U.S.A.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/26278/1/0000363.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0376-6349(86)90016-7en_US
dc.identifier.sourceJournal of Occupational Accidentsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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