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|a 10.1080/02664763.2023.2249636
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|a eng
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|a Workman, Austin
|e verfasserin
|4 aut
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|a Spatial analysis for interval-valued data
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 30.08.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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|a 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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|a Journal Article
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|a 62H11
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|a Spatial prediction
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| 650 |
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|a geostatistics
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| 650 |
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|a interval-valued data
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| 650 |
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|a symbolic data analysis
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| 650 |
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|a temperature
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| 700 |
1 |
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|a Song, Joon Jin
|e verfasserin
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 51(2024), 10 vom: 28., Seite 1946-1960
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