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Document type:
Zeitungsartikel 
Author(s):
Bueno-Larraz, B. and Klepsch, J. 
Title:
Variable selection for the prediction of C[0,1]-valued AR processes using RKHS 
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:
Pages contribution:
139-153 
Language:
en 
WWW:
_blank 
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