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Titel:

Estimating invertible functional time series

Dokumenttyp:
Buchbeitrag
Autor(en):
Aue, A. and Klepsch, J.
Abstract:
This contribution discusses the estimation of an invertible functional time series through fitting of functional moving average processes. The method uses a functional version of the innovations algorithm and dimension reduction onto a number of principal directions. Several methods are suggested to automate the procedures. Empirical evidence is presented in the form of simulations and an application to traffic data.
Seitenangaben Beitrag:
51-58
Stichworte:
Order Selection, Functional Principal Component Analysis, Functional Principal Component, Principal Subspace, Innovation Algorithm
Herausgeber:
Aneiros, G, Bongiorno, E.G., Cao, R. and Vieu, P.
Buchtitel:
Functional Statistics and Related Fields.
Verlag / Institution:
Springer
Publikationsdatum:
28.04.2017
Jahr:
2017
Quartal:
2. Quartal
Jahr / Monat:
2017-04
Monat:
Apr
Print-ISBN:
978-3319558455
Sprache:
en
WWW:
Springer
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Semester:
SS 17
Format:
Text
 BibTeX