Nearest-neighbor sparse Cholesky matrices in spatial statistics
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Publication:6602373
DOI10.1002/wics.1574zbMATH Open1544.62036MaRDI QIDQ6602373
Publication date: 11 September 2024
Published in: Wiley Interdisciplinary Reviews. WIREs Computational Statistics (Search for Journal in Brave)
spatial statisticssparse methodsnearest neighbor Gaussian processCholesky matrixlarge geospatial data
Cites Work
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Comparing composite likelihood methods based on pairs for spatial Gaussian random fields
- Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
- A comparison of spatial predictors when datasets could be very large
- Generalized bootstrap method for assessment of uncertainty in semivariogram inference
- Improving the performance of predictive process modeling for large datasets
- A close look at the spatial structure implied by the CAR and SAR models.
- High-dimensional Bayesian geostatistics
- Multivariate spatial meta kriging
- Stochastic approximation and its applications
- Compactly supported correlation functions
- Challenging the curse of dimensionality in multivariate local linear regression
- A case study competition among methods for analyzing large spatial data
- Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models
- Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
- Consistency of random forests
- Maximum likelihood estimation of models for residual covariance in spatial regression
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Fast and Exact Simulation of Stationary Gaussian Processes through Circulant Embedding of the Covariance Matrix
- Spatial Modeling With Spatially Varying Coefficient Processes
- Approximating Likelihoods for Large Spatial Data Sets
- Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments†
- Large Multi-scale Spatial Modeling Using Tree Shrinkage Priors
- Neighborhood Dependence in Bayesian Spatial Models
- Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
- Random forests
- A general framework for Vecchia approximations of Gaussian processes
- BRISC: bootstrap for rapid inference on spatial covariances
- On nearest-neighbor Gaussian process models for massive spatial data
- Permutation and Grouping Methods for Sharpening Gaussian Process Approximations
- Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets
- An additive approximate Gaussian process model for large spatio-temporal data
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