Estimation and Prediction of Functional Autoregressive Processes
Author:
Tahar Mourid a
| Affiliation: | a Universit de Tlemcen, Algeria. |
DOI:
10.1080/02331880212048
Publication Frequency:
6 issues per year
Subjects:
Mathematical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Formats available:
PDF
(English)
View Article:
View Article (PDF)
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 |

Download Citation


de Tlemcen, Algeria.
CiteULike
Del.icio.us
BibSonomy
Connotea