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Exploration and Inference in Spatial Extremes Using Empirical Basis Functions.

Authors :
Morris, Samuel A.
Reich, Brian J.
Thibaud, Emeric
Source :
Journal of Agricultural, Biological & Environmental Statistics (JABES). Dec2019, Vol. 24 Issue 4, p555-572. 18p.
Publication Year :
2019

Abstract

Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model spatial extremal dependence. Based on a low-rank max-stable model, we propose a data-driven approach to estimate meaningful basis functions using empirical pairwise extremal coefficients. These spatial empirical basis functions can be used to visualize the main trends in extremal dependence. In addition to exploratory analysis, we describe how these functions can be used in a Bayesian hierarchical model to model spatial extremes of large datasets. We illustrate our methods on extreme precipitations in eastern USA. Supplementary materials accompanying this paper appear online [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*INFERENTIAL statistics

Details

Language :
English
ISSN :
10857117
Volume :
24
Issue :
4
Database :
Academic Search Index
Journal :
Journal of Agricultural, Biological & Environmental Statistics (JABES)
Publication Type :
Academic Journal
Accession number :
139232645
Full Text :
https://doi.org/10.1007/s13253-019-00359-1