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Article: Depth functions as measures of representativeness

TitleDepth functions as measures of representativeness
Authors
Issue Date2014
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00362/index.htm
Citation
Statistical Papers, 2014, v. 55 n. 4, p. 1079-1105 How to Cite?
AbstractData depth provides a natural means to rank multivariate vectors with respect to an underlying multivariate distribution. Most existing depth functions emphasize a centre-outward ordering of data points, which may not provide a useful geometric representation of certain distributional features, such as multimodality, of concern to some statistical applications. Such inadequacy motivates us to develop a device for ranking data points according to their “representativeness” rather than “centrality” with respect to an underlying distribution of interest. Derived essentially from a choice of goodness-of-fit test statistic, our device calls for a new interpretation of “depth” more akin to the concept of density than location. It copes particularly well with multivariate data exhibiting multimodality. In addition to providing depth values for individual data points, depth functions derived from goodness-of-fit tests also extend naturally to provide depth values for subsets of data points, a concept new to the data-depth literature.
Persistent Identifierhttp://hdl.handle.net/10722/189450
ISSN
2015 Impact Factor: 0.781
2015 SCImago Journal Rankings: 0.976
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDong, Y-
dc.contributor.authorLee, SMS-
dc.date.accessioned2013-09-17T14:41:47Z-
dc.date.available2013-09-17T14:41:47Z-
dc.date.issued2014-
dc.identifier.citationStatistical Papers, 2014, v. 55 n. 4, p. 1079-1105-
dc.identifier.issn0932-5026-
dc.identifier.urihttp://hdl.handle.net/10722/189450-
dc.description.abstractData depth provides a natural means to rank multivariate vectors with respect to an underlying multivariate distribution. Most existing depth functions emphasize a centre-outward ordering of data points, which may not provide a useful geometric representation of certain distributional features, such as multimodality, of concern to some statistical applications. Such inadequacy motivates us to develop a device for ranking data points according to their “representativeness” rather than “centrality” with respect to an underlying distribution of interest. Derived essentially from a choice of goodness-of-fit test statistic, our device calls for a new interpretation of “depth” more akin to the concept of density than location. It copes particularly well with multivariate data exhibiting multimodality. In addition to providing depth values for individual data points, depth functions derived from goodness-of-fit tests also extend naturally to provide depth values for subsets of data points, a concept new to the data-depth literature.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00362/index.htm-
dc.relation.ispartofStatistical Papers-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s00362-013-0555-5-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleDepth functions as measures of representativeness-
dc.typeArticle-
dc.identifier.emailLee, SMS: smslee@hku.hk-
dc.identifier.authorityLee, SMS=rp00726-
dc.description.naturepostprint-
dc.identifier.doi10.1007/s00362-013-0555-5-
dc.identifier.hkuros223126-
dc.identifier.volume55-
dc.identifier.issue4-
dc.identifier.spage1079-
dc.identifier.epage1105-
dc.identifier.isiWOS:000343046200011-
dc.publisher.placeGermany-

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