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dc.contributor.authorHlávka, Zdeněk
dc.contributor.authorMeintanis, Simos G.
dc.contributor.authorHušková, Marie
dc.date.accessioned2018-04-11T13:31:06Z
dc.date.available2018-04-11T13:31:06Z
dc.date.issued2017
dc.identifier.citationHlávka, Z. et al. 2017. Change point detection with multivariate observations based on characteristic functions. (In Ferger, D., Manteiga, W.G., Schmidt, T. & Wang, J.-L., eds. From statistics to mathematical finance: Festschrift in honour of Winfried Stute: 273-290. [https://doi.org/10.1007/978-3-319-50986-0_14]en_US
dc.identifier.isbn978-3-319-50985-3
dc.identifier.issn978-3-319-50986-0 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/26684
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-50986-0_14
dc.identifier.urihttps://doi.org/10.1007/978-3-319-50986-0_14
dc.description.abstractWe consider break-detection procedures for vector observations, both under independence as well as under an underlying structural time series scenario. The new methods involve L2-type criteria based on empirical characteristic functions. Asymptotic as well as Monte-Carlo results are presented. The new methods are also applied to time-series data from the financial sectoren_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.titleChange point detection with multivariate observations based on characteristic functionsen_US
dc.typeBook chapteren_US
dc.contributor.researchID21262977 - Meintanis, Simos George


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