Predicting extreme surges from sparse data using a copula‐based hierarchical Bayesian spatial model

Beck, Nicholas; Genest, Christian; Jalbert, Jonathan; Mailhot, Mélina

doi:10.1002/env.2616

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…Climate scientists are beginning to consider compound extreme events, sometimes in collaboration with statisticians (e.g., Huang, Monahan & Zwiers, 2021), but we are still only at the early stages of being able to effectively apply the tools for multivariate extreme value analysis to which Canadians (e.g., Genest & Rivest, 1989; Genest & Favre, 2007; Joe, Li & Nikoloulopoulos, 2010) have made key foundational contributions. Some very impressive advances are also being made in the area of spatial extremes, such as Beck et al (2020).…”

Section: Current and Future Directionsmentioning

confidence: 99%

Canadian contributions to environmetrics

et al. 2022

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This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.Résumé: Cet article souligne l'ampleur de la collaboration entre les scientifiques canadiens dans le domaine de la statistique et présente un survol des travaux réalisés par des spécialistes du sujet sur un ensemble de thèmes liés aux sciences de l'environnement. Les auteurs relatent différents moyens par lesquels cette collaboration s'est incarnée et a permis aux Canadiennes et Canadiens de contribuer à l'environnemétrie et à divers domaines connexes, notamment les sciences du climat, la foresterie et les pêches, dont l'importance est capitale pour la gestion des ressources naturelles du pays.

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Section: Current and Future Directionsmentioning

confidence: 99%

Canadian contributions to environmetrics

et al. 2022

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“…These models are commonly used in the analysis of censored survival times. This family of distributions includes a number of other distributions also appearing in environmental studies including the extreme value distribution used by Beck et al (2020) and Panagoulia et al (2014); the Pareto distribution used in Huang et al (2019); Gumbel and Pareto distributions used in Gouet et al (2020). The exponential-gamma distribution appearing in Bopp and Shaby (2017) is not in this family, however.…”

Section: Introductionmentioning

confidence: 99%

Regression methods for the appearances of extremes in climate data

Bláha

Kane

et al. 2022

Environmetrics

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For any given city, on any calendar day, there will be record high and low temperatures. Which record occurred earlier? If there is a trend towards warming then, intuitively, there should be a preponderance of record highs occurring more recently than the record lows for each of the 365 calendar days. We are interested in modeling the joint distribution of appearances of the extremes but not these values themselves. We develop a bivariate discrete distribution modeling the joint indices of maximum and minimum in a sequence of independent random variables sampled from different distributions. We assume these distributions share a proportional hazard rate and develop regression methods for these paired values. This approach has reasonable power to detect a small mean change over a decade. Using readily available public data, we examine the daily calendar extreme values of five US cities for the decade 2011–2020. We develop linear regression models for these data, describe models to account for calendar‐date dependence, and use diagnostic measures to detect remarkable observations.

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“…An alternative to Gaussian process-based approaches is to apply a copula for the joint distribution of the underlying spatial process. A copula (Joe, 2014) is used to characterize the dependence structure between random variables, separately from the specification of marginal distributions, so it has been used to describe non-Gaussian spatial variability with general non-Gaussian marginals; see, e.g., Bárdossy (2006), Ghosh and Mallick (2011), Beck et al (2020). However, copulas need to be used with careful consideration of their properties in a spatial setting.…”

Section: Introductionmentioning

confidence: 99%

Nearest-Neighbor Geostatistical Models for Non-Gaussian Data

Zheng¹,

Kottas²,

Sansó³

2021

Preprint

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We develop a class of nearest neighbor mixture transition distribution process (NNMP) models that provides flexibility and scalability for non-Gaussian geostatistical data. We use a directed acyclic graph to define a proper spatial process with finite-dimensional distributions given by finite mixtures. We develop conditions to construct general NNMP models with pre-specified stationary marginal distributions. We also establish lower bounds for the strength of the tail dependence implied by NNMP models, demonstrating the flexibility of the proposed methodology for modeling multivariate dependence through bivariate distribution specification. To implement inference and prediction, we formulate a Bayesian hierarchical model for the data, using the NNMP prior model for the spatial random effects process. From an inferential point of view, the NNMP model lays out a new computational approach to handling large spatial data sets, leveraging the mixture model structure to avoid computational issues that arise from large matrix operations. We illustrate the benefits of the NNMP modeling framework using synthetic data examples and through analysis of sea surface temperature data from the Mediterranean sea.

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Predicting extreme surges from sparse data using a copula‐based hierarchical Bayesian spatial model

Cited by 12 publications

References 40 publications

Canadian contributions to environmetrics

Canadian contributions to environmetrics

Regression methods for the appearances of extremes in climate data

Nearest-Neighbor Geostatistical Models for Non-Gaussian Data

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