Blackbox Kriging: Spatial Prediction Without Specifying Variogram Models

By Ronald P. Barry, Jay M. Ver Hoef. 1996

How researchers see this paper

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Gaussian Process Regression Networks (2011, background): “The results for LMC, CMOGP, MFITC, MPITC, and MDTC are reproduced from Alvarez and Lawrence (2011).”
A Moving Average Approach for Spatial Statistical Models of Stream Networks (2010, background): “KEY WORDS: Euclidean distance; Geostatistics; Kernel convolution; Spatial autocorrelation; Spatial linear model.”
Process (2020, methodology): “We adopt the tail-up spatial model [32, 33, 34] to capture the spatial correlation between two locations u, v ∈ K at time t denoted as αt(u, v).”
Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing (2005, methodology): “In this regard, there are the nonparametric variogram (cid:12)tting approaches, e.g., Shapiro and Botha (1991) and Barry and Ver Hoef (1996).”
Space and Space-Time Modeling using Process Convolutions (2002, background): “# generate K matricies for each scale K1_matrix(NA,ncol=m1[1],nrow=n) for(i in 1:m1[1]) K1[,i]_dnorm(s,mean=w[[1]][i],sd=sdkern[1])”