Show simple item record

dc.contributor.authorHlávka, Zdeněk
dc.contributor.authorMeintanis, Simos G.
dc.contributor.authorHušková, Marie
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. []en_US
dc.identifier.issn978-3-319-50986-0 (Online)
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.titleChange point detection with multivariate observations based on characteristic functionsen_US
dc.typeBook chapteren_US
dc.contributor.researchID21262977 - Meintanis, Simos George

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record