Aufgrund von Vorbereitungen auf eine neue Version von KOPS, können am Montag, 6.2. und Dienstag, 7.2. keine Publikationen eingereicht werden. (Due to preparations for a new version of KOPS, no publications can be submitted on Monday, Feb. 6 and Tuesday, Feb. 7.)
Type of Publication: | Journal article |
Publication status: | Published |
Author: | Beran, Jan; Telkmann, Klaus |
Year of publication: | 2018 |
Published in: | Communications in Statistics : Theory and Methods ; 47 (2018), 22. - pp. 5460-5473. - ISSN 0361-0926. - eISSN 1532-415X |
DOI (citable link): | https://dx.doi.org/10.1080/03610926.2017.1395048 |
Summary: |
We consider nonparametric estimation of the density function and its derivatives for multivariate linear processes with long-range dependence. In a first step, the asymptotic distribution of the multivariate empirical process is derived. In a second step, the asymptotic distribution of kernel density estimators and their derivatives is obtained.
|
Subject (DDC): | 510 Mathematics |
Keywords: | kernel density estimation, linear process, multivariate, long-range dependence, multivariate time series |
Bibliography of Konstanz: | Yes |
Refereed: | Yes |
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
BERAN, Jan, Klaus TELKMANN, 2018. On nonparametric density estimation for multivariate linear long-memory processes. In: Communications in Statistics : Theory and Methods. 47(22), pp. 5460-5473. ISSN 0361-0926. eISSN 1532-415X. Available under: doi: 10.1080/03610926.2017.1395048
@article{Beran2018-11-17nonpa-41236, title={On nonparametric density estimation for multivariate linear long-memory processes}, year={2018}, doi={10.1080/03610926.2017.1395048}, number={22}, volume={47}, issn={0361-0926}, journal={Communications in Statistics : Theory and Methods}, pages={5460--5473}, author={Beran, Jan and Telkmann, Klaus} }
<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/123456789/41236"> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-02-05T15:47:15Z</dc:date> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/41236"/> <dc:contributor>Telkmann, Klaus</dc:contributor> <dcterms:issued>2018-11-17</dcterms:issued> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:title>On nonparametric density estimation for multivariate linear long-memory processes</dcterms:title> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Telkmann, Klaus</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-02-05T15:47:15Z</dcterms:available> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> <dcterms:abstract xml:lang="eng">We consider nonparametric estimation of the density function and its derivatives for multivariate linear processes with long-range dependence. In a first step, the asymptotic distribution of the multivariate empirical process is derived. In a second step, the asymptotic distribution of kernel density estimators and their derivatives is obtained.</dcterms:abstract> <dc:contributor>Beran, Jan</dc:contributor> <dc:creator>Beran, Jan</dc:creator> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> </rdf:Description> </rdf:RDF>