record.view.types.journal-article external.repositories.badge: scopus, orcid, cienciavitae
Extracting information from interval data using symbolic principal component analysis
Austrian Journal of Statistics
— 2017
Key information
record.authors:
fields.record.dateIssued.label
0amte.12e1/1/2017per0.0o0ent.for0amt.yeamr.0ont12.0amy
fields.record.abstract.label
<jats:p>We introduce generic definitions of symbolic variance and covariance for random interval-valued variables, that lead to a unified and insightful interpretation of four known symbolic principal component estimation methods: CPCA, VPCA, CIPCA, and SymCovPCA. Moreover, we propose the use of truncated versions of symbolic principal components, that use a strict subset of the original symbolic variables, as a way to improve the interpretation of symbolic principal components. Furthermore, the analysis of a real dataset leads to a meaningful characterization of Internet traffic applications, while highligting similarities between the symbolic principal component estimation methods considered in the paper.</jats:p>
Publication details
record.authors.institution:
António Manuel Pacheco Pires
ist12634
Rui Jorge Morais Tomaz Valadas
ist126537
fields.record.version.label
AO - fields.record.version.values.AO
fields.record.citationTitle.label
Austrian Journal of Statistics
fields.record.citationStartPage.label
79
fields.record.citationEndPage.label
87
fields.record.citationVolume.label
46
fields.record.citationIssue.label
3-4SpecialIssue
fields.record.issn.label
1026-597X
fields.record.doi.label
fields.record.subjectFos.label
mathematics - fields.record.subjectFos.values.mathematics
fields.record.languageIso.label
eng - fields.record.languageIso.values.eng
fields.record.identifierUri.label
http://dx.doi.org/10.17713/ajs.v46i3-4.673
fields.record.rights.label:
fields.record.rights.values.metadata-only-access