Conditions on which cokriging does not do better than kriging

DOI10.1016/j.jmva.2022.105084OpenAlexW4286250150MaRDI QIDQ2079621

Chae Young Lim, Wei-Ying Wu

Publication date: 30 September 2022

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2022.105084

zbMATH Keywords

spatial predictionspatial data analysismultivariate spatial processescoregionalization covariance model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• When doesn't cokriging outperform Kriging?
• Nonstationary modeling for multivariate spatial processes
• Interpolation of spatial data. Some theory for kriging
• Estimating deformations of stationary processes
• Reducing non-stationary random fields to stationarity and isotropy using a space deformation
• A copula model for non-Gaussian multivariate spatial data
• Nonstationary multivariate process modeling through spatially varying coregionalization
• Estimating deformations of isotropic Gaussian random fields on the plane
• Multivariate spatial modeling for geostatistical data using convolved covariance functions
• Normal Variance-Mean Mixtures and z Distributions
• Matérn Cross-Covariance Functions for Multivariate Random Fields
• Multivariate spatial covariance models: a conditional approach
• Universal co-kriging under intrinsic coregionalization
• A new form of the co-kriging equations

This page was built for publication: Conditions on which cokriging does not do better than kriging