Imperial College London
Alternate

ProfessorAlmutVeraart

Faculty of Natural Sciences, Department of Mathematics

Head of the Statistics Section, Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8545a.veraart Website

 
 
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Location

 

551Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nguyen:2017:10.1016/j.spasta.2017.03.006,
author = {Nguyen, M and Veraart, A},
doi = {10.1016/j.spasta.2017.03.006},
journal = {Spatial Statistics},
pages = {148--190},
title = {Modelling spatial heteroskedasticity by volatility modulated moving averages},
url = {http://dx.doi.org/10.1016/j.spasta.2017.03.006},
volume = {20},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Nguyen,M
AU  - Veraart,A
DO  - 10.1016/j.spasta.2017.03.006
EP  - 190
PY  - 2017///
SN  - 2211-6753
SP  - 148
TI  - Modelling spatial heteroskedasticity by volatility modulated moving averages
T2  - Spatial Statistics
UR  - http://dx.doi.org/10.1016/j.spasta.2017.03.006
UR  - http://hdl.handle.net/10044/1/46124
VL  - 20
ER  -
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