Exploration and Inference in Spatial Extremes Using Empirical Basis Functions

Samuel A. Morris (), Brian J. Reich and Emeric Thibaud
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Samuel A. Morris: North Carolina State University
Brian J. Reich: North Carolina State University
Emeric Thibaud: Ecole Polytechnique Fédérale de Lausanne

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 4, No 1, 555-572

Abstract: 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

Keywords: Dimension reduction; Max-stable process; Non-stationary data analysis (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13253-019-00359-1

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