Toggle Main Menu Toggle Search

ePrints

Spatio-temporal analysis with short- and long-memory dependence: a state-space approach

Lookup NU author(s): Professor Emilio Porcu

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This expansion is well known in time series analysis and can be extended straightforwardly in space–time. Such an approach allows easy implementation of the Kalman filter procedure for estimation and prediction of linear time processes exhibiting both short- and long-range dependence and a spatial dependence structure given on the locations. Furthermore, we consider a truncated state-space equation, which allows to calculate an approximate likelihood for large data sets. The performance of the proposed Kalman filter approach is evaluated by means of several Monte Carlo experiments implemented under different scenarios, and it is illustrated with two applications.


Publication metadata

Author(s): Ferreira G, Mateu J, Porcu E

Publication type: Article

Publication status: Published

Journal: TEST

Year: 2018

Volume: 27

Issue: 1

Pages: 221-245

Print publication date: 01/03/2018

Online publication date: 10/05/2017

Acceptance date: 27/04/2017

ISSN (print): 1133-0686

ISSN (electronic): 1863-8260

Publisher: Springer

URL: https://doi.org/10.1007/s11749-017-0541-7

DOI: 10.1007/s11749-017-0541-7


Altmetrics

Altmetrics provided by Altmetric


Actions

    Link to this publication


Share