On spatial skew-Gaussian processes and applications

被引：70
|
作者：
Zhang, Hao [1 ]
El-Shaarawi, Abdel [2 ]
机构：
[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA
[2] Environm Canada, Natl Water Res Inst, Burlington, ON L7R 4A6, Canada
来源：
ENVIRONMETRICS | 2010年 / 21卷 / 01期
基金：
美国国家科学基金会;
关键词：
EM algorithm; Matern covariogram; skew-normal distribution; skew-Gaussian process; slice sampling; LINEAR MIXED MODELS; BAYESIAN PREDICTION; DISTRIBUTIONS; ALGORITHMS; FIELDS;
D O I：
10.1002/env.982
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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 (C) 2009 John Wiley & Sons, Ltd.
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页码：33 / 47
页数：15
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