5. Statistics for Spatial Data

By Mike Rees, Noel Cressie. 1993

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Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network (2009, methodology): “The covariance function satisfies C(h) = C(0) − γ(h), where C(0) is the variance of e, equal to the sill of the variogram and γ(h) is the variogram of e (Cressie, 1993; Christensen, 1991).”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, background): “Note that kriging with the so-called “Nugget effect” (Cressie, 1993) can also be a non-interpolating approximator that produces a statistics-based bias at design sites and serves as a smoother.”
Spatial correlation patterns in coastal environmental variables and survival rates of salmon in the north‐east Pacific Ocean (2002, background): “The estimation of any covariance function assumes that covariance is a function of relative distance only and does not vary in space or over time (Cressie, 1993).”
Small Area Estimation‐New Developments and Directions (2002, background): “, Cressie 1993), but are not in wide use for SAE problems. As few exceptions we mention the articles by Datta and Ghosh (1991), Datta et el.”
On the Validity of Commonly Used Covariance and Variogram Functions on the Sphere (2011, methodology): “Parallel to the intrinsically stationary process on Rn (Cressie 1993), we have the following.”