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Activity Number: 131 - Methods for Spatial, Temporal, and Spatio-Temporal Data
Type: Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #318149
Title: Spatial Analysis of Nonstationary Spatial Interval-Valued Data
Author(s): Austin Workman* and Joon Jin Song
Companies: Baylor University and Baylor University
Keywords: Spatial Prediction; Interval-valued Data; Symbolic Data Analysis; Kriging with External Drift; Temperature

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.

Authors who are presenting talks have a * after their name.

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