The screening effect in Kriging

By Michael L. Stein. 2002

135 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/94e72092b08fc026b431f97b861467cd40266ff3

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In Civil and Structural Engineering

In Environmental Engineering

In Soil Science

Citations in the corpus

The kriging update equations and their application to the selection of neighboring data (2009, background): “The justification for such an approach is the so-called screening effect, according to which closest data screen out the influence of data located farthest away [6, 14, 18, 21 ].”
2010 Rietz lecture: When does the screening effect hold? (2011, methodology): “Specifically, Stein (2002) proves (1) when, essentially, for some x0 ∈Rd not in the integer lattice, Fε is all points of the form ε(x0 + J) for J in the integer lattice, Nε is the restriction of Fε to some fixed region with 0 in its interior and f is regularly varying at infinity [Bingham, Goldie…”
Covariance Tapering for Interpolation of Large Spatial Datasets (2006, background): “Stein (2002) discussed this so-called screening effect in the case of regular lattices.”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, methodology): “This phenomenon is often called screening eﬀect [Cressie (1993); Stein (2002)], and is used to compute fast predictions in the case of large data sets.”
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets (2014, background): “TheMatérn covariance function is monotonically decreasing with distance and satisfies theoretical screening conditions, that is, the ability to predict accurately based on a few neighbors (Stein 2002).”
Random Forests for dependent data (2020, background): “Such assumptions on the dimensionality and/or regularity of design has been widely used for theoretical studies of spatial processes [23, 67].”