Covariance tapering for prediction of large spatial data sets in transformed random fields

DOI10.1007/s10463-013-0399-8zbMath1273.62233OpenAlexW2241258481MaRDI QIDQ380011

Toshihiro Hirano, Yoshihiro Yajima

Publication date: 11 November 2013

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-013-0399-8

zbMATH Keywords

krigingspatial statisticsHermite polynomialsspectral densitytransformed random fields

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Random fields; image analysis (62M40)

Related Items (4)

A multi-resolution approximation via linear projection for large spatial datasets ⋮ Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics ⋮ A case study competition among methods for analyzing large spatial data ⋮ Asymptotic properties of rank estimators in a simple spatial linear regression model under spatial sampling designs

Uses Software

• DLMF
• spam

Cites Work

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