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Interval estimation of the mean response in a log-regression model

dc.contributor.authorWu, Jianrongen_US
dc.contributor.authorWong, A. C. M.en_US
dc.contributor.authorWei, Weien_US
dc.date.accessioned2007-07-11T18:15:43Z
dc.date.available2007-07-11T18:15:43Z
dc.date.issued2006-06-30en_US
dc.identifier.citationWu, Jianrong; Wong, A. C. M.; Wei, Wei (2006). "Interval estimation of the mean response in a log-regression model." Statistics in Medicine 25(12): 2125-2135. <http://hdl.handle.net/2027.42/55233>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55233
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16220472&dopt=citationen_US
dc.description.abstractA standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest. El-Shaarawi and Viveros developed an interval estimation of the mean response of a log-regression model based on large sample theory. There is however very little information available in the literature on constructing such estimates when the sample size is small. In this paper, we develop a small-sample corrected interval by using the likelihood-based inference method developed by Barndorff-Nielson and Fraser et al . Simulation results show that the proposed interval provides almost exact coverage probability, even for small samples. Copyright © 2005 John Wiley & Sons, Ltd.en_US
dc.format.extent115491 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.subject.otherMathematics and Statisticsen_US
dc.titleInterval estimation of the mean response in a log-regression modelen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Biostatistics, St Jude Children's Research Hospital, 332 North Lauderdale St., Memphis, TN 38105, U.S.A. ; Department of Biostatistics, St Jude Children's Research Hospital, 332 North Lauderdale Street, Memphis, TN 38105, U.S.A.en_US
dc.contributor.affiliationotherSASIT, Atkinson Faculty of Liberal and Professional Studies, York University, 4700 Keele St., North York, Ontario, Canada M3J 1P3en_US
dc.identifier.pmid16220472en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55233/1/2329_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/sim.2329en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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