Modelling spatial heteroskedasticity by volatility modulated moving averages

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Title: Modelling spatial heteroskedasticity by volatility modulated moving averages
Authors: Nguyen, M
Veraart, A
Item Type: Journal Article
Abstract: Spatial heteroskedasticity has been observed in many spatial data applications such as air pollution and vegetation. We propose a model, the volatility modulated moving average, to account for changing variances across space. This stochastic process is driven by Gaussian noise and involves a stochastic volatility field. It is conditionally non-stationary but unconditionally stationary: a useful property for theory and practice. We develop a discrete convolution algorithm as well as a two-step moments-matching estimation method for simulation and inference respectively. These are tested via simulation experiments and the consistency of the estimators is proved under suitable double asymptotics. To illustrate the advantages that this model has over the usual Gaussian moving average or process convolution, sea surface temperature anomaly data from the International Research Institute for Climate and Society are analysed.
Issue Date: 4-Apr-2017
Date of Acceptance: 28-Mar-2017
ISSN: 2211-6753
Publisher: Elsevier
Start Page: 148
End Page: 190
Journal / Book Title: Spatial Statistics
Volume: 20
Copyright Statement: © 2017Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Sponsor/Funder: Commission of the European Communities
Imperial College London
Funder's Grant Number: FP7-PEOPLE-2012-CIG-321707
Publication Status: Published
Appears in Collections:Mathematics
Faculty of Natural Sciences

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