Journal article
Nonparametric spatial models for extremes: application to extreme temperature data
Extremes, Vol.16(1), pp.75-101
03/01/2013
DOI: 10.1007/s10687-012-0154-1
PMCID: PMC3775382
PMID: 24058280
Abstract
Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although the tools for statistical modeling of univariate extremes are well-developed, extending these tools to model spatial extreme data is an active area of research. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models. We apply our methods to annual maximum temperature values in the east-south-central United States.
Details
- Title: Subtitle
- Nonparametric spatial models for extremes: application to extreme temperature data
- Creators
- Montserrat Fuentes - North Carolina State UniversityJohn HenryBrian Reich
- Resource Type
- Journal article
- Publication Details
- Extremes, Vol.16(1), pp.75-101
- DOI
- 10.1007/s10687-012-0154-1
- PMID
- 24058280
- PMCID
- PMC3775382
- NLM abbreviation
- Extremes (Boston)
- ISSN
- 1386-1999
- eISSN
- 1572-915X
- Language
- English
- Date published
- 03/01/2013
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Provost Office Administration
- Record Identifier
- 9983759097602771
Metrics
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