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Activity Number: 111 - Application and Development of Statistical Methods for Spatio-Temporal Data
Type: Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #322703
Title: Bayesian Analysis for Spatial Interval-Valued Data
Author(s): Austin Workman* and Joon Jin Song
Companies: Baylor University and Baylor Univeristy
Keywords: Bayesian; Spatial Prediction; Interval-Valued Data; Symbolic Data Analysis

Spatial data has been observed and recorded in various forms in numerous applications. Symbolic data analysis (SDA) is an emerging approach for aggregating large data sets into multi-valued forms, such as intervals, histograms, and lists. In this paper, we propose Bayesian methodologies for analyzing spatial interval-valued data (SIVD). We compare the prediction accuracy of the methods using proposed prediction performance metrics and examine prior sensitivity with different prior distributions. A real data set is used to illustrate the methods.

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

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