Spatial analysis for interval-valued data

被引：0
|
作者：
Workman, Austin [1 ]
Song, Joon Jin [1 ]
机构：
[1] Baylor Univ, Dept Stat Sci, Waco, TX 76798 USA
来源：
JOURNAL OF APPLIED STATISTICS | 2024年 / 51卷 / 10期
关键词：
Spatial prediction; interval-valued data; temperature; symbolic data analysis; geostatistics; REGRESSION; MODELS;
D O I：
10.1080/02664763.2023.2249636
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：1946 / 1960
页数：15
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