Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data

DOI10.1016/j.csda.2020.107081OpenAlexW3081731186MaRDI QIDQ830598

Matthias Katzfuss, Daniel Zilber

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.07828

zbMATH Keywords

exponential familygeostatisticsKrigingnearest neighborsparse inverse Choleskyspatial generalized linear mixed model

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (9)

Bayesian inference for high-dimensional nonstationary Gaussian processes ⋮ A multi-resolution approximation via linear projection for large spatial datasets ⋮ Bayesian multiresolution modeling of georeferenced data: an extension of `LatticeKrig' ⋮ Scaled Vecchia Approximation for Fast Computer-Model Emulation ⋮ Fast Bayesian inference of block nearest neighbor Gaussian models for large data ⋮ Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations ⋮ Nearest-neighbor mixture models for non-Gaussian spatial processes ⋮ Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide ⋮ Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering

Uses Software

• R
• GMRFLib
• FRK
• GPvecchia

Cites Work

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• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors
• Improving the performance of predictive process modeling for large datasets
• Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion)
• Randomized sketches for kernels: fast and optimal nonparametric regression
• A case study competition among methods for analyzing large spatial data
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Bayesian inference for generalized linear mixed models
• A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
• Fixed Rank Kriging for Very Large Spatial Data Sets
• Gaussian Predictive Process Models for Large Spatial Data Sets
• Accurate Approximations for Posterior Moments and Marginal Densities
• Generalized Nested Dissection
• Model-Based Geostatistics
• Fast and Exact Simulation of Stationary Gaussian Processes through Circulant Embedding of the Covariance Matrix
• Approximating Likelihoods for Large Spatial Data Sets
• Gaussian Markov Random Fields
• Approximate Inference in Generalized Linear Mixed Models
• Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments^†
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
• Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets
• Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm

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