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Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations including graphs that illuminate the theory discussed and over 1, references.

Fully balancing theory with applications, Statistics for Spatial Data , Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology. NOEL A.

He received his PhD in statistics from Princeton University. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Skip to Main Content.

First published: 10 September Valid Covariograms. Valid Variograms. Variogram Model Fitting. Criteria for Fitting a Variogram Model. Least Squares. Properties of Variogram-Parameter Estimators. Cross-Validating the Fitted Variogram.

Spatial Prediction and Kriging. Scale of Variation. Ordinary Kriging. Effect of Variogram Parameters on Kriging. Lognormal and Trans-Gaussian Kriging. Some Final Remarks.

Statistics for Spatial Data (Wiley Series in Probability and Statistics)

Robust Kriging. Universal Kriging. Universal Kriging of Coal-Ash Data. Trend-Surface Prediction. Estimating the Variogram for Universal Kriging. Bayesian Kriging. Kriging Revisited.

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Median-Polish Kriging. Gridded Data. Nongridded Data.

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Median-Based covariogram Estimators are Less Biased. Geostatistical Data, Simulated and Real. Simulation of Spatial Processes. Conditional Simulation. Applications of Geostatistics. Wolf camp-Aquifer Data.

Spatial statistics / Brian D. Ripley | National Library of Australia

Intrinsic-Stationarity Assumption. Nonconstant-Mean Assumption. Soil-Water Tension Data. Soil-Water-Infiltration Data. Estimating and Modeling the Spatial Dependence. Sudden-Infant-Death-Syndrome Data. Wheat-Yield Data. Presence of Trend in the Data. Median-Polish Robust Kriging. Acid-Deposition Data. Spatial Modeling and Prediction.

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Sampling Design. Space-Time Geostatistical Data. Special Topics in Statistics for Spatial Data. Nonlinear Geostatistics. Change of Support. Stability of the Geostatistical Method. Estimation of Spatial-Dependence Parameters. Stability of the Kriging Predictor. Stability of the Kriging Variance. Intrinsic Random Functions of Order k.. Applications of the Theory of Random Processes. Spatial Design. Spatial Sampling Design. Spatial Experimental Design. Field Trials. Nearest-Neighbor Analyses. Analyses Based on Spatial Modeling.

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Baafi and N. Spatio-temporal statistical modeling of livestock waste in streams. Journal of Agricultural, Biological, and Environmental Statistics , 2 , Non-point-source pollution of surface waters over a watershed , in Statistics for the Environment 3: Pollution Assessment and Control , eds V. Barnett and K.

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