Titelaufnahme

Titel
Detecting structural breaks in eigensystems of functional time series / Holger Dette, Tim Kutta
VerfasserDette, Holger ; Kutta, Tim
ErschienenDortmund : Universitätsbibliothek Dortmund, November 2019
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (38 Seiten) : Diagramme
SerieDiscussion paper ; Nr. 27/2019
URNurn:nbn:de:hbz:6:2-122759 
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
Dateien
Detecting structural breaks in eigensystems of functional time series [0.61 mb]
Zusammenfassung

Detecting structural changes in functional data is a prominent topic in statistical literature. However not all trends in the data are important in applications, but only those of large enough in uence. In this paper we address the problem of identifying relevant changes in the eigenfunctions and eigenvalues of covariance kernels of L[0, 1]-valued time series. By self-normalization techniques we derive pivotal, asymptotically consistent tests for relevant changes in these characteristics of the second order structure and investigate their nite sample properties in a simulation study. The applicability of our approach is demonstrated analyzing German annual temperature data.

Klassifikation
Links
Nachweis
Statistik
Das PDF-Dokument wurde 0 mal heruntergeladen.