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Estimation and Prediction of Functional Autoregressive Processes 

Author: Tahar Mourid a
Affiliation:   a Universiteacute de Tlemcen, Algeria.
DOI: 10.1080/02331880212048
Publication Frequency: 6 issues per year
Published in: journal Statistics, Volume 36, Issue 2 2002 , pages 125 - 138
Formats available: PDF (English)
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Abstract

We present a generalization of some previous works (Bosq, Mourid, Pumo) about the functional forecast of a Banach autoregressive processes. We are mainly concerned with order p , p >1, autoregressive processes which appear to be a natural extension of the well-known R d -valued autoregressive processes to a functional framework. This modelization provides an new approach for estimating and for predicting a continuous time stochastic process over an entire time interval. Using results from [12] we prove asymptotic properties of estimators of the parameters and predictors which are based upon a principal component decomposition of a Hilbert-Schmidt operator with unknown eigenvectors.
Keywords: Banach Autoregressive Process; Covariance Operator; Estimation; Prediction; Mixing
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