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Statistics for Spatial Data.
TLDR
Statistics for Spatial Data GEOSTATISTICAL DATA Geostatistics Spatial Prediction and Kriging Applications of Spatial Models on Lattices Inference for Lattice Models References Author Index Subject Index. Expand
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Statistics for Spatial Data, Revised Edition.
TLDR
Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. Expand
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Statistics for Spatial Data: Cressie/Statistics
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Multinomial goodness-of-fit tests
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Statistics for Spatio-Temporal Data
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Fitting variogram models by weighted least squares
The method of weighted least squares is shown to be an appropriate way of fitting variogram models. The weighting scheme automatically gives most weight to early lags and down-weights those lags withExpand
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Fixed rank kriging for very large spatial data sets
Spatial statistics for very large spatial data sets is challenging. The size of the data set, "n", causes problems in computing optimal spatial predictors such as kriging, since its computationalExpand
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5. Statistics for Spatial Data
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The origins of kriging
In this article, kriging is equated with spatial optimal linear prediction, where the unknown random-process mean is estimated with the best linear unbiased estimator. This allows early appearancesExpand
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Robust estimation of the variogram: I
It is a matter of common experience that ore values often do not follow the normal (or lognormal) distributions assumed for them, but, instead, follow some other heavier-tailed distribution. In thisExpand
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