A kernel approximation to the kriging predictor of a spatial process

DOI10.1007/BF00116469zbMath0761.62127MaRDI QIDQ1207613

Michael L. Stein

Publication date: 1 April 1993

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

zbMATH Keywords

Kelvin functionnonparametric regressionseries expansionskernel approximationgeneralized covariance functionkriging predictoroptimal linear predictororder 2 thin plate smoothing splinetwo-dimensional spatial process

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Density estimation (62G07)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Spline smoothing: The equivalent variable kernel method
• Asymptotics for M-type smoothing splines
• Asymptotically efficient prediction of a random field with a missspecified covariance function
• Multivariate interpolation at arbitrary points made simple
• Periodic splines and spectral estimation
• Some results on Tchebycheffian spline functions and stochastic processes
• Multivariate Smoothing Spline Functions
• An application of the theory of equivalence of Gaussian measures to a prediction problem
• The intrinsic random functions and their applications
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Integraltafel
• Some asymptotic properties of kriging when the covariance function is misspecified

This page was built for publication: A kernel approximation to the kriging predictor of a spatial process