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Activity Number:
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131
- Methods for Spatial, Temporal, and Spatio-Temporal Data
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #318149
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Title:
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Spatial Analysis of Nonstationary Spatial Interval-Valued Data
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Author(s):
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Austin Workman* and Joon Jin Song
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Companies:
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Baylor University and Baylor University
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Keywords:
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Spatial Prediction;
Interval-valued Data;
Symbolic Data Analysis;
Kriging with External Drift;
Temperature
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Abstract:
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Spatial data has been progressively observed and recorded in various forms in numerous applications. Symbolic data analysis (SDA) is an approach to analyzing massive or complex data aggregated into multi-valued forms, such as intervals, histograms, and lists. In this paper, we focus on non-stationary spatial interval-valued data (SIVD). We propose kriging with external drift (KED) methodologies for spatial prediction of SIVD with scalar and interval-valued covariates. A simulation study is performed to compare prediction performance. We illustrate the utility of the proposed methods by applying them to real-world SIVD dataset.
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Authors who are presenting talks have a * after their name.
