New Exploratory Tools for Extremal Dependence: $$\chi $$ χ Networks and Annual Extremal Networks

Whitney K. Huang (), Daniel S. Cooley (), Imme Ebert-Uphoff (), Chen Chen () and Snigdhansu Chatterjee ()
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Whitney K. Huang: University of Victoria
Daniel S. Cooley: Colorado State University
Imme Ebert-Uphoff: Colorado State University
Chen Chen: University of Chicago
Snigdhansu Chatterjee: University of Minnesota

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 3, No 6, 484-501

Abstract: Abstract Understanding dependence structure among extreme values plays an important role in risk assessment in environmental studies. In this work, we propose the $$\chi $$ χ network and the annual extremal network for exploring the extremal dependence structure of environmental processes. A $$\chi $$ χ network is constructed by connecting pairs whose estimated upper tail dependence coefficient, $${{\hat{\chi }}}$$ χ ^ , exceeds a prescribed threshold. We develop an initial $$\chi $$ χ network estimator, and we use a spatial block bootstrap to assess both the bias and variance of our estimator. We then develop a method to correct the bias of the initial estimator by incorporating the spatial structure in $$\chi $$ χ . In addition to the $$\chi $$ χ network, which assesses spatial extremal dependence over an extended period of time, we further introduce an annual extremal network to explore the year-to-year temporal variation of extremal connections. We illustrate the $$\chi $$ χ and the annual extremal networks by analyzing the hurricane season maximum precipitation at the US Gulf Coast and surrounding area. Analysis suggests there exists long distance extremal dependence for precipitation extremes in the study region and the strength of the extremal dependence may depend on some regional scale meteorological conditions, for example, sea surface temperature.

Keywords: External dependence; Networks; Hurricanes; Spatial extremes; Precipitation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13253-019-00356-4

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