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On spatial skew‐Gaussian processes and applications
10.1002/env.982.abs
In many applications, observed spatial variables have skewed distributions. It is often of interest to model the shape of the skewed marginal distributions as well as the spatial correlations. We propose a class of stationary processes that have skewed marginal distributions. The covariance function of the process can be given explicitly. We study maximum likelihood inference through a Monte Carlo EM algorithm, and develop a method for the minimum mean‐square error prediction. We also present two applications of the process. Copyright © 2009 John Wiley & Sons, Ltd.
On spatial skew‐Gaussian processes and applications
10.1002/env.982.abs
In many applications, observed spatial variables have skewed distributions. It is often of interest to model the shape of the skewed marginal distributions as well as the spatial correlations. We propose a class of stationary processes that have skewed marginal distributions. The covariance function of the process can be given explicitly. We study maximum likelihood inference through a Monte Carlo EM algorithm, and develop a method for the minimum mean‐square error prediction. We also present two applications of the process. Copyright © 2009 John Wiley & Sons, Ltd.
On spatial skew‐Gaussian processes and applications
Zhang, Hao (author) / El‐Shaarawi, Abdel (author)
Environmetrics ; 21 ; 33-47
2010-02-01
15 pages
Article (Journal)
Electronic Resource
English
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