Abstract
Due to the inherent variance heterogeneity in clustered preferential sampling, the underlying variogram cannot be estimated directly. A variance-stabilizing declustering method is proposed here using a modified Box–Cox transformation. In contrast to the traditional Box–Cox transformation that aims at achieving normally distributed data, its modified version has the objective to match the variance in clustered sample observations to the variance of the remaining more dispersed background sample observations. The proposed approach leads to predictions with lower standard errors than alternative proposed methods.
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Pu, X., Tiefelsdorf, M. (2017). A Variance-Stabilizing Transformation to Mitigate Biased Variogram Estimation in Heterogeneous Surfaces with Clustered Samples. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_24
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DOI: https://doi.org/10.1007/978-3-319-22786-3_24
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