The Annals of Applied Statistics

Nonstationary covariance models for global data

Mikyoung Jun and Michael L. Stein

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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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Ann. Appl. Stat. Volume 2, Number 4 (2008), 1271-1289.

First available in Project Euclid: 8 January 2009

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


Jun, Mikyoung; Stein, Michael L. Nonstationary covariance models for global data. Ann. Appl. Stat. 2 (2008), no. 4, 1271--1289. doi:10.1214/08-AOAS183.

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