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Max-Stability at Work (or Not): Estimating Return Levels for Daily Rainfall Data Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Geostatistical Analysis in Extremes: An Overview

Author : M. Manuela Neves

Published in: Mathematics of Energy and Climate Change

Publisher: Springer International Publishing

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Abstract

Classical statistics of extremes is very well developed in the univariate context for modeling and estimating parameters of rare events. Whenever rain, snow, storms, hurricanes, earthquakes, and so on, happen the analysis of extremes is of primordial importance. However such rare events often present a temporal aspect, a spatial aspect or both. Classical geostatistics, widely used for spatial data, is mostly based on multivariate normal distribution, inappropriate for modeling tail behavior. The analysis of spatial extreme data, an active research area, lies at the intersection of two statistical domains: extreme value theory and geostatistics. Some statistical tools are already available for the spatial modeling of extremes, including Bayesian hierarchical models, copulas and max-stable random fields. The purpose of this chapter is to present an overview of basic spatial analysis of extremes, in particular reviewing max-stable processes. A real case study of annual maxima of daily rainfall measurements in the North of Portugal is slightly discussed as well the main functions in R environment for doing such analysis.

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Metadata
Title
Geostatistical Analysis in Extremes: An Overview
Author
M. Manuela Neves
Copyright Year
2015
Book
Mathematics of Energy and Climate Change

Print ISBN: 978-3-319-16120-4

Electronic ISBN: 978-3-319-16121-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16121-1_10

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