Evangelou, E. and Zhu, Z., 2012. Optimal predictive design augmentation for spatial generalised linear mixed models. Journal of Statistical Planning and Inference, 142 (12), pp. 3242-3253.
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.
|Item Type ||Articles|
|Creators||Evangelou, E.and Zhu, Z.|
|Departments||Faculty of Science > Mathematical Sciences|
|Publisher Statement||des_augment.pdf: NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Statistical Planning and Inference. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Statistical Planning and Inference, vol 142, isue 12, 2012, DOI 10.1016/j.jspi.2012.05.008|
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