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Computational Statistics in Climatology$
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Ilya Polyak

Print publication date: 1996

Print ISBN-13: 9780195099997

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195099997.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 23 June 2021

Multivariate AR Processes

Multivariate AR Processes

Chapter:
5 (p.208) Multivariate AR Processes
Source:
Computational Statistics in Climatology
Author(s):

Ilya Polyak

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195099997.003.0007

This chapter is devoted to different computational aspects of multivariate modeling. An algorithm for fitting such models with sequential increasing of the order is given. Several examples of approximation of multiple climatological time series by first-order AR models are considered. It is emphasized that such modeling can be used not only for forecasting, but also for the analysis of the parameter matrix as a matrix of the interactions and feedbacks of the observed processes. Some problems in identifying stochastic climate models are also discussed. It is shown that formulation of climatology problems within the strict frameworks of fundamental theory will facilitate natural progress along with the development of these methods.

Keywords:   circulation statistics, climate system identification, g-variate white noise, matrix correlation function, matrix covariance function, multivariate AR processes, q-variate AR process, qr-variate correlation function

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