Development and evaluation of geostatistical methods for non-Euclidean-based spatial covariance matrices

DOI10.1007/S11004-019-09791-YzbMath1421.86017OpenAlexW2921756942WikidataQ63922459 ScholiaQ63922459MaRDI QIDQ2323497

Benjamin J. K. Davis, Frank C. Curriero

Publication date: 3 September 2019

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905632

zbMATH Keywords

kriginggeostatisticsmulti-dimensional scalingnon-Euclidean distancespositive-definite covariance matriceswater salinity

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Geostatistics (86A32) Prediction theory (aspects of stochastic processes) (60G25)

Related Items (1)

Properties and comparison of some kriging sub-model aggregation methods

Uses Software

• Unnamed Item
• ArcMap
• geoR
• ggplot2
• ArcGIS Desktop
• e1071
• RandomFields
• Splancs
• gdistance
• sfsmisc
• rgdal
• GitHub
• matrixcalc

Cites Work

• Unnamed Item
• Unnamed Item
• Computing the nearest correlation matrix--a problem from finance
• Model-based geostatistics.
• On the use of non-Euclidean distance measures in geostatistics
• Asymptotic Expressions for the Unit-Step and Dirac Delta Functions
• A Modified Cholesky Algorithm Based on a Symmetric Indefinite Factorization

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