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On aggregation of strongly dependent time series

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BERAN, Jan, Haiyan LIU, Sucharita GHOSH, 2020. On aggregation of strongly dependent time series. In: Scandinavian Journal of Statistics. Wiley. 47(3), pp. 690-710. ISSN 0303-6898. eISSN 1467-9469. Available under: doi: 10.1111/sjos.12421

@article{Beran2020-09aggre-48631, title={On aggregation of strongly dependent time series}, year={2020}, doi={10.1111/sjos.12421}, number={3}, volume={47}, issn={0303-6898}, journal={Scandinavian Journal of Statistics}, pages={690--710}, author={Beran, Jan and Liu, Haiyan and Ghosh, Sucharita} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:available rdf:datatype="">2020-02-13T12:25:08Z</dcterms:available> <dc:contributor>Beran, Jan</dc:contributor> <dcterms:isPartOf rdf:resource=""/> <dc:contributor>Liu, Haiyan</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dspace:hasBitstream rdf:resource=""/> <dc:language>eng</dc:language> <dcterms:title>On aggregation of strongly dependent time series</dcterms:title> <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 International</dc:rights> <dcterms:issued>2020-09</dcterms:issued> <dcterms:hasPart rdf:resource=""/> <dc:creator>Liu, Haiyan</dc:creator> <dc:creator>Beran, Jan</dc:creator> <dc:date rdf:datatype="">2020-02-13T12:25:08Z</dc:date> <dc:contributor>Ghosh, Sucharita</dc:contributor> <bibo:uri rdf:resource=""/> <dspace:isPartOfCollection rdf:resource=""/> <dc:creator>Ghosh, Sucharita</dc:creator> <dcterms:rights rdf:resource=""/> <dcterms:abstract xml:lang="eng">We consider cross‐sectional aggregation of time series with long‐range dependence. This question arises for instance from the statistical analysis of networks where aggregation is defined via routing matrices. Asymptotically, aggregation turns out to increase dependence substantially, transforming a hyperbolic decay of autocorrelations to a slowly varying rate. This effect has direct consequences for statistical inference. For instance, unusually slow rates of convergence for nonparametric trend estimators and nonstandard formulas for optimal bandwidths are obtained. The situation changes, when time‐dependent aggregation is applied. Suitably chosen time‐dependent aggregation schemes can preserve a hyperbolic rate or even eliminate autocorrelations completely.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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