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A normed measures of variability among proportions

dc.contributor.authorCoffey, Mary P.en_US
dc.contributor.authorFeingold, Marciaen_US
dc.contributor.authorBromberg, Judithen_US
dc.date.accessioned2006-04-07T20:06:37Z
dc.date.available2006-04-07T20:06:37Z
dc.date.issued1988-12en_US
dc.identifier.citationCoffey, Mary P., Feingold, Marcia, Bromberg, Judith (1988/12)."A normed measures of variability among proportions." Computational Statistics &amp; Data Analysis 7(2): 127-141. <http://hdl.handle.net/2027.42/27023>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6V8V-45F5VBR-1B/2/47c28dcf8aed1d48253db374c1658c12en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/27023
dc.description.abstractA measure of variability among a set of proportions is developed. There are no distributional assumptions, so the measure, H, is applicable in a wide variety of situations. H is scaled so that its range is zero (all proportions are equal) to one (maximum variability among the proportions, given the weighted average). The measure can be interpreted as a distance: the value of H indicates the position of the proportions relative to the possible extremes of no variability or maximum variability. Any set of constant weights can be applied to the proportions: the weights are used to compute the weighted average proportion and are also used to determine the extent to which each proportion affects the variability measure. Comparisons are made to other measures of variability and a numerical example is given.en_US
dc.format.extent993854 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleA normed measures of variability among proportionsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, The University of Michigan, Ann Arbor, MI 48109, USAen_US
dc.contributor.affiliationotherDepartment of Mathematical Sciences, Oakland University, Rochester, MI 48309, USAen_US
dc.contributor.affiliationotherDivision of Biostatistics and Research Epidemiology, Henry Ford Hospital, Detroit, MI 48202, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/27023/1/0000011.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0167-9473(88)90088-6en_US
dc.identifier.sourceComputational Statistics &amp; Data Analysisen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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