Estimating high-resolution red sea surface temperature hotspots, using a low-rank semiparametric spatial model

DOI10.1214/20-AOAS1418zbMath1478.62355arXiv1912.05657OpenAlexW3179069572MaRDI QIDQ2245130

Arnab Hazra, Raphaël Huser

Publication date: 15 November 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.05657

zbMATH Keywords

Bayesian inferenceDirichlet process mixture modelextreme eventlow-rank methodcovariance and tail dependencenonstationary meansea surface temperature dataStudent's \(t\) process

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Hydrology, hydrography, oceanography (86A05)

Related Items

A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes ⋮ Distributed Bayesian inference in massive spatial data ⋮ High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields ⋮ Modeling spatial tail dependence with Cauchy convolution processes

Uses Software

• GMRFLib
• rARPACK
• Rfast

Cites Work

• Unnamed Item
• Unnamed Item
• Efficient inference and simulation for elliptical Pareto processes
• A hierarchical max-stable spatial model for extreme precipitation
• Spatio-temporal exceedance locations and confidence regions
• Extremal \(t\) processes: elliptical domain of attraction and a spectral representation
• Geostatistics of dependent and asymptotically independent extremes
• The \(L_{1}\)-consistency of Dirichlet mixtures in multivariate Bayesian density estimation
• A loss function approach to identifying environmental exceedances
• Interpolation of spatial data. Some theory for kriging
• Markov chain Monte Carlo based on deterministic transformations
• INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles
• Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes
• Projecting flood-inducing precipitation with a Bayesian analogue model
• Continuous spatial process models for spatial extreme values
• Spatial hierarchical modeling of precipitation extremes from a regional climate model
• A case study competition among methods for analyzing large spatial data
• A spliced gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
• The t Copula and Related Copulas
• Generalized Spatial Dirichlet Process Models
• Gaussian Predictive Process Models for Large Spatial Data Sets
• High-dimensional peaks-over-threshold inference
• Approximating Likelihoods for Large Spatial Data Sets
• Gaussian Markov Random Fields
• A dimension-reduced approach to space-time Kalman filtering
• A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
• Modeling Spatial Processes with Unknown Extremal Dependence Class
• Likelihood-Based Inference for Max-Stable Processes
• Approximate Likelihood for Large Irregularly Spaced Spatial Data
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Excursion and Contour Uncertainty Regions for Latent Gaussian Models
• Fundamentals of Nonparametric Bayesian Inference
• Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing
• A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation
• Statistical modeling of spatial extremes
• A Space-Time Skew-t Model for Threshold Exceedances
• Credible regions for exceedance sets of geostatistical data