Parallel cross-validation: a scalable fitting method for Gaussian process models

DOI10.1016/j.csda.2020.107113OpenAlexW3094493083MaRDI QIDQ829752

Florian Gerber, Douglas W. Nychka

Publication date: 6 May 2021

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

Full work available at URL: https://arxiv.org/abs/1912.13132

zbMATH Keywords

krigingspatial statisticscross-validationhigh-performance computingGaussian random fields

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Statistical test for anomalous diffusion based on empirical anomaly measure for Gaussian processes

Uses Software

• R
• lhs
• spBayes
• pbdR
• pbdMPI

Cites Work

• Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
• A comparison of generalized cross validation and modified maximum likelihood for estimating the parameters of a stochastic process
• A survey of cross-validation procedures for model selection
• Interpolation of spatial data. Some theory for kriging
• Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
• The screening effect in kriging
• Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
• Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
• Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping

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