User: Guest  Login
Title:

Variable selection for the prediction of C[0,1]-valued AR processes using RKHS

Document type:
Zeitungsartikel
Author(s):
Bueno-Larraz, B. and Klepsch, J.
Abstract:
A model for the prediction of functional time series is introduced, where observations are assumed to be realizations of a C[0,1]-valued process. We model the dependence of the data with a non-standard autoregressive structure, motivated in terms of the Reproducing Kernel Hilbert Space (RKHS) generated by the covariance kernel of the data. The general definition has as particular case a set of finite-dimensional models based on marginal variables of the process. Thus, this approach is especially...     »
Keywords:
Autoregressive (AR); Continuous functions; Functional data analysis (FDA); Functional linear process; Prediction; Variable selection; RKHS
Dewey Decimal Classification:
510 Mathematik
Journal title:
Technometrics
Year:
2019
Journal volume:
61
Journal issue:
2
Pages contribution:
139-153
Language:
en
Fulltext / DOI:
doi:10.1080/00401706.2018.1505660
Print-ISSN:
0040-1706
E-ISSN:
1537-2723
Notes:
Published online: 29 Oct 2018
Status:
Verlagsversion / published
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX