A Cluster-Based Outlier Detection Scheme for Multivariate Data

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

JOBE, J. Marcus, Michael POKOJOVY, 2015. A Cluster-Based Outlier Detection Scheme for Multivariate Data. In: Journal of the American Statistical Association. 110(512), pp. 1543-1551. ISSN 0162-1459. eISSN 1537-274X. Available under: doi: 10.1080/01621459.2014.983231

@article{Jobe2015Clust-29413, title={A Cluster-Based Outlier Detection Scheme for Multivariate Data}, year={2015}, doi={10.1080/01621459.2014.983231}, number={512}, volume={110}, issn={0162-1459}, journal={Journal of the American Statistical Association}, pages={1543--1551}, author={Jobe, J. Marcus and Pokojovy, Michael} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/29413"> <dc:contributor>Jobe, J. Marcus</dc:contributor> <dc:language>eng</dc:language> <dc:creator>Jobe, J. Marcus</dc:creator> <dc:creator>Pokojovy, Michael</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-12-10T12:56:04Z</dc:date> <dc:contributor>Pokojovy, Michael</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-12-10T12:56:04Z</dcterms:available> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> <dcterms:abstract xml:lang="eng">Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate data set of interest. To overcome this masking effect, we propose a computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw’s minimum covariance determinant method with a multi-step cluster-based algorithm that initially filters out potential masking points. Compared to the most robust procedures, simulation studies show that our new method is better for outlier detection. Additional real data comparisons are given.</dcterms:abstract> <dcterms:title>A Cluster-Based Outlier Detection Scheme for Multivariate Data</dcterms:title> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/29413"/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:issued>2015</dcterms:issued> </rdf:Description> </rdf:RDF>

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto