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On the asymmetry of biological frequency distributions

dc.contributor.authorSchork, Nicholas J.en_US
dc.contributor.authorWeder, Alan B.en_US
dc.contributor.authorSchork, M. Anthonyen_US
dc.date.accessioned2006-04-28T17:01:14Z
dc.date.available2006-04-28T17:01:14Z
dc.date.issued1990en_US
dc.identifier.citationSchork, Nicholas J.; Weder, Alan B.; Schork, M. Anthony (1990)."On the asymmetry of biological frequency distributions." Genetic Epidemiology 7(6): 427-446. <http://hdl.handle.net/2027.42/38499>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/38499
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=2292368&dopt=citationen_US
dc.description.abstractThe long-standing problem of determining whether the skewness in a sample frequency distribution is the manifestation of the intermixing of disparate groups characterizable by a normal mixture distribution or the manifestation of non-mixture, skew-producing determinants is discussed. Biometrical tools for modeling and quantifying the significance of the skewness in a trait of interest that invite interpretations other than those formed in mixtures or “subgroups” are elaborated. Statistical methods for testing whether a normal mixture distribution better characterizes a set of data than the proposed (or any other) skewed, single-population-oriented models are offered. The power of these tests is examined through Monte Carlo experimentation. A brief application in hypertension research demonstrates some of the problems and methods discussed in the paper.en_US
dc.format.extent1198307 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherLife and Medical Sciencesen_US
dc.subject.otherGeneticsen_US
dc.titleOn the asymmetry of biological frequency distributionsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbsecondlevelGeneticsen_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDivision of Hypertension, Department of Medicine, University of Michigan, Ann Arbor ; Department of Statistics, University of Michigan, Ann Arbor ; Division of Hypertension, Department of Medicine, University of Michigan, R6592 Kresge I, Ann Arbor, MI 48109-0500en_US
dc.contributor.affiliationumDivision of Hypertension, Department of Medicine, University of Michigan, Ann Arboren_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arboren_US
dc.identifier.pmid2292368en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/38499/1/1370070605_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/gepi.1370070605en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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