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Spatial analysis for interval-valued data
Austin Workman1, Joon Jin Song1
1Department of Statistical Science, Baylor University, Waco, TX, USA.
Abstract:
Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide geostatistical methods for spatial prediction, predictive performance measure for prediction assessment, and visualization for mapping SIVD. The proposed methods are illustrated with both simulated and real examples.
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