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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 
Journal title:
Preprint 
Year:
2017 
Year / month:
2017-10 
Quarter:
4. Quartal 
Month:
Oct 
Language:
en 
WWW:
_blank 
Status:
Preprint / submitted 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text