Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

12-2012

Journal or Book Title

Journal of Statistical Planning and Inference

Volume

142

Issue

12

First Page

3242

Last Page

3253

DOI

10.1016/j.jspi.2012.05.008

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.

Comments

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Published Version

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