Optimal predictive design augmentation for spatial generalised linear mixed models

被引:8
|
作者
Evangelou, Evangelos [1 ]
Zhu, Zhengyuan [2 ]
机构
[1] Univ Bath, Dept Math Sci, Bath BA2 7AY, Avon, England
[2] Iowa State Univ, Dept Stat, Ames, IA USA
基金
美国国家科学基金会;
关键词
Generalised linear mixed models; Geostatistics; Predictive inference; Sampling design; APPROXIMATE BAYESIAN-INFERENCE; MAXIMUM-LIKELIHOOD; GEOSTATISTICS; COVARIANCE; PARAMETERS; NETWORKS; ERROR;
D O I
10.1016/j.jspi.2012.05.008
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A typical model for geostatistical data when the observations are counts is the spatial generalised linear mixed model. We present a criterion for optimal sampling design under this framework which aims to minimise the error in the prediction of the underlying spatial random effects. The proposed criterion is derived by performing an asymptotic expansion to the conditional prediction variance. We argue that the mean of the spatial process needs to be taken into account in the construction of the predictive design, which we demonstrate through a simulation study where we compare the proposed criterion against the widely used space-filling design. Furthermore, our results are applied to the Norway precipitation data and the rhizoctonia disease data. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:3242 / 3253
页数:12
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