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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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Multivariate AR Processes

Multivariate AR Processes

5 (p.208) Multivariate AR Processes
Computational Statistics in Climatology

Ilya Polyak

Oxford University Press

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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