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December 2008 Nonstationary covariance models for global data
Mikyoung Jun, Michael L. Stein
Ann. Appl. Stat. 2(4): 1271-1289 (December 2008). DOI: 10.1214/08-AOAS183

Abstract

With the widespread availability of satellite-based instruments, many geophysical processes are measured on a global scale and they often show strong nonstationarity in the covariance structure. In this paper we present a flexible class of parametric covariance models that can capture the nonstationarity in global data, especially strong dependency of covariance structure on latitudes. We apply the Discrete Fourier Transform to data on regular grids, which enables us to calculate the exact likelihood for large data sets. Our covariance model is applied to global total column ozone level data on a given day. We discuss how our covariance model compares with some existing models.

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Mikyoung Jun. Michael L. Stein. "Nonstationary covariance models for global data." Ann. Appl. Stat. 2 (4) 1271 - 1289, December 2008. https://doi.org/10.1214/08-AOAS183

Information

Published: December 2008
First available in Project Euclid: 8 January 2009

zbMATH: 1168.62381
MathSciNet: MR2655659
Digital Object Identifier: 10.1214/08-AOAS183

Keywords: fast Fourier transform , Nonstationary covariance function , processes on spheres , TOMS ozone data

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.2 • No. 4 • December 2008
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