Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures
The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniqu...
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ftdatacite:10.48550/arxiv.1506.07836 2023-05-15T16:11:51+02:00 Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures Thibaud, Emeric Aalto, Juha Cooley, Daniel S. Davison, Anthony C. Heikkinen, Juha 2015 https://dx.doi.org/10.48550/arxiv.1506.07836 https://arxiv.org/abs/1506.07836 unknown arXiv https://dx.doi.org/10.1214/16-aoas980 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Methodology stat.ME Applications stat.AP FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2015 ftdatacite https://doi.org/10.48550/arxiv.1506.07836 https://doi.org/10.1214/16-aoas980 2022-04-01T12:13:44Z The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this paper we exploit a case in which the full likelihood of a Brown-Resnick process can be calculated, using componentwise maxima and their partitions in terms of individual events, and we propose two new approaches to inference. The first estimates the partitions using declustering, while the second uses random partitions in a Markov chain Monte Carlo algorithm. We use these approaches to construct a Bayesian hierarchical model for extreme low temperatures in northern Fennoscandia. Text Fennoscandia DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
spellingShingle |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences Thibaud, Emeric Aalto, Juha Cooley, Daniel S. Davison, Anthony C. Heikkinen, Juha Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
topic_facet |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
description |
The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this paper we exploit a case in which the full likelihood of a Brown-Resnick process can be calculated, using componentwise maxima and their partitions in terms of individual events, and we propose two new approaches to inference. The first estimates the partitions using declustering, while the second uses random partitions in a Markov chain Monte Carlo algorithm. We use these approaches to construct a Bayesian hierarchical model for extreme low temperatures in northern Fennoscandia. |
format |
Text |
author |
Thibaud, Emeric Aalto, Juha Cooley, Daniel S. Davison, Anthony C. Heikkinen, Juha |
author_facet |
Thibaud, Emeric Aalto, Juha Cooley, Daniel S. Davison, Anthony C. Heikkinen, Juha |
author_sort |
Thibaud, Emeric |
title |
Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
title_short |
Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
title_full |
Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
title_fullStr |
Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
title_full_unstemmed |
Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures |
title_sort |
bayesian inference for the brown-resnick process, with an application to extreme low temperatures |
publisher |
arXiv |
publishDate |
2015 |
url |
https://dx.doi.org/10.48550/arxiv.1506.07836 https://arxiv.org/abs/1506.07836 |
genre |
Fennoscandia |
genre_facet |
Fennoscandia |
op_relation |
https://dx.doi.org/10.1214/16-aoas980 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1506.07836 https://doi.org/10.1214/16-aoas980 |
_version_ |
1765997041201184768 |