ArticleThe annals of applied statistics2012
A HIERARCHICAL MAX-STABLE SPATIAL MODEL FOR EXTREME PRECIPITATION.
Article in The annals of applied statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States.Scientific data · 2025Article
- Valid model-free spatial prediction.Journal of the American Statistical Association · 2024Article
- Article
- How do mobility restrictions and social distancing during COVID-19 affect oil price?Journal of statistical theory and practice · 2022Article
- Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Probabilistic reanalysis of storm surge extremes in Europe.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Usable Science for Managing the Risks of Sea-Level Rise.Earth's future · 2019Article
- On spatial conditional extremes for ocean storm severity.Environmetrics · 2019Article
- A space-time skew-t model for threshold exceedances.Biometrics · 2017Article
- A marginal cure rate proportional hazards model for spatial survival data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2015Article
- Spatial extreme value analysis to project extremes of large-scale indicators for severe weather.Environmetrics · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.