Multi-scale shotgun stochastic search for large spatial datasets

DOI10.1016/j.csda.2020.106931OpenAlexW3005778102MaRDI QIDQ2178160

Bruno Sansó, Daniel Kirsner

Publication date: 7 May 2020

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

Full work available at URL: https://doi.org/10.1016/j.csda.2020.106931

zbMATH Keywords

Gaussian processparallel computingdiscrete kernel convolutionnonstationary spatial models

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Nearest-neighbor mixture models for non-Gaussian spatial processes

Uses Software

• GMRFLib
• SSS
• fields
• RandomFields
• LatticeKrig

Cites Work

• Unnamed Item
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Conditionally linear models for non-homogeneous spatial random fields
• Spatial variation of total column ozone on a global scale
• High-dimensional Bayesian geostatistics
• Mixtures ofg-Priors in Generalized Linear Models
• Handbook of Spatial Statistics
• Gaussian Predictive Process Models for Large Spatial Data Sets
• Mixtures of g Priors for Bayesian Variable Selection
• Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
• Gaussian Markov Random Fields
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Modeling Nonstationary Processes Through Dimension Expansion
• Large Multi-scale Spatial Modeling Using Tree Shrinkage Priors
• Shotgun Stochastic Search for “Largep” Regression
• Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective
• Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

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