On nonparametric regression for bivariate circular long-memory time series

Loading...
Thumbnail Image
Date
2022
Authors
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Journal article
Publication status
Published
Published in
Statistical Papers ; 63 (2022), 1. - pp. 29-52. - Springer. - ISSN 0932-5026. - eISSN 1613-9798
Abstract
We consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.
Summary in another language
Subject (DDC)
510 Mathematics
Keywords
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690BERAN, Jan, Britta STEFFENS, Sucharita GHOSH, 2022. On nonparametric regression for bivariate circular long-memory time series. In: Statistical Papers. Springer. 63(1), pp. 29-52. ISSN 0932-5026. eISSN 1613-9798. Available under: doi: 10.1007/s00362-021-01228-1
BibTex
@article{Beran2022-02nonpa-53410,
  year={2022},
  doi={10.1007/s00362-021-01228-1},
  title={On nonparametric regression for bivariate circular long-memory time series},
  number={1},
  volume={63},
  issn={0932-5026},
  journal={Statistical Papers},
  pages={29--52},
  author={Beran, Jan and Steffens, Britta and Ghosh, Sucharita}
}
RDF
<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/server/rdf/resource/123456789/53410">
    <dc:creator>Beran, Jan</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53410/1/Beran_2-qyhrecwhqg129.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53410"/>
    <dcterms:abstract xml:lang="eng">We consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.</dcterms:abstract>
    <dcterms:issued>2022-02</dcterms:issued>
    <dc:contributor>Steffens, Britta</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-04-20T09:23:37Z</dc:date>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dcterms:title>On nonparametric regression for bivariate circular long-memory time series</dcterms:title>
    <dc:language>eng</dc:language>
    <dc:contributor>Beran, Jan</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53410/1/Beran_2-qyhrecwhqg129.pdf"/>
    <dc:contributor>Ghosh, Sucharita</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Steffens, Britta</dc:creator>
    <dc:creator>Ghosh, Sucharita</dc:creator>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-04-20T09:23:37Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
  </rdf:Description>
</rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
URL of original publication
Test date of URL
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed
Unknown