File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: The influence of observations on misclassification probability in multiple discriminant analysis

TitleThe influence of observations on misclassification probability in multiple discriminant analysis
Authors
KeywordsInfluence function
Influential observations
Misclassification probability
Multiple discriminant analysis
Omission approach
Issue Date1996
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.asp
Citation
Communications In Statistics - Theory And Methods, 1996, v. 25 n. 8, p. 1917-1930 How to Cite?
AbstractThe influence of observations in estimating the misclassification probability in multiple discriminant analysis is studied using the common omission approach. An empirical influence function for the misclassification probability is also derived. It can give a very good approximation to the omission approach, but the computational load is much reduced. Various extensions of the measures are suggested. The proposed measures are applied to the famous Iris data set. The same three observations are identified as having the most influence under different measures. Copyright © 1996 by Marcel Dekker, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/82821
ISSN
2015 Impact Factor: 0.3
2015 SCImago Journal Rankings: 0.518
References

 

DC FieldValueLanguage
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:33:47Z-
dc.date.available2010-09-06T08:33:47Z-
dc.date.issued1996en_HK
dc.identifier.citationCommunications In Statistics - Theory And Methods, 1996, v. 25 n. 8, p. 1917-1930en_HK
dc.identifier.issn0361-0926en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82821-
dc.description.abstractThe influence of observations in estimating the misclassification probability in multiple discriminant analysis is studied using the common omission approach. An empirical influence function for the misclassification probability is also derived. It can give a very good approximation to the omission approach, but the computational load is much reduced. Various extensions of the measures are suggested. The proposed measures are applied to the famous Iris data set. The same three observations are identified as having the most influence under different measures. Copyright © 1996 by Marcel Dekker, Inc.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.aspen_HK
dc.relation.ispartofCommunications in Statistics - Theory and Methodsen_HK
dc.subjectInfluence functionen_HK
dc.subjectInfluential observationsen_HK
dc.subjectMisclassification probabilityen_HK
dc.subjectMultiple discriminant analysisen_HK
dc.subjectOmission approachen_HK
dc.titleThe influence of observations on misclassification probability in multiple discriminant analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0361-0926&volume=25&spage=1917&epage=1930&date=1996&atitle=The+influence+of+observation+on+misclassification+probability+in+multiple+discriminant+analysisen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0011873006en_HK
dc.identifier.hkuros26838en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0011873006&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue8en_HK
dc.identifier.spage1917en_HK
dc.identifier.epage1930en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats