Optimal predictive design augmentation for spatial generalised linear mixed models

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2012-12-01
Authors
Evangelou, Evangelos
Zhu, Zhengyuan
Zhu, Zhengyuan
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Altmetrics
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Statistics
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Statistics
Abstract

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.

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This article is published as Evangelou, Evangelos, and Zhengyuan Zhu. "Optimal predictive design augmentation for spatial generalised linear mixed models." Journal of Statistical Planning and Inference 142, no. 12 (2012): 3242-3253. DOI: 10.1016/j.jspi.2012.05.008. Posted with permission.

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