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Statistics for spatial data /
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2015.
|
| Edición: | Revised edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Contents; Preface; Acknowledgments; 1. Statistics for Spatial Data; 1.1 Spatial Data and Spatial Models; 1.2 Introductory Examples; 1.2.1 Geostatistical Data; 1.2.2 Lattice Data; 1.2.3 Point Patterns; 1.3 Statistics for Spatial Data: Why?; PART I GEOSTATISTICAL DATA; 2. Geostatistics; 2.1 Continuous Spatial Index; 2.2 Spatial Data Analysis of Coal Ash in Pennsylvania; 2.2.1 Intrinsic Stationarity; 2.2.2 Square-Root-Differences Cloud; 2.2.3 The Pocket Plot; 2.2.4 Decomposing the Data into Large- and Small-Scale Variation; 2.2.5 Analysis of Residuals.
- 2.2.6 Variogram of Residuals from Median Polish2.3 Stationary Processes; 2.3.1 Variogram; 2.3.2 Covariogram and Correlogram; 2.4 Estimation of the Variogram; 2.4.1 Comparison of Variogram and Covariogram Estimation; 2.4.2 Exact Distribution Theory for the Variogram Estimator; 2.4.3 Robust Estimation of the Variogram; 2.5 Spectral Representations; 2.5.1 Valid Covariograms; 2.5.2 Valid Variograms; 2.6 Variogram Model Fitting; 2.6.1 Criteria for Fitting a Variogram Model; 2.6.2 Least Squares; 2.6.3 Properties of Variogram-Parameter Estimators; 2.6.4 Cross-Validating the Fitted Variogram.
- 3. Spatial Prediction and Kriging3.1 Scale of Variation; 3.2 Ordinary Kriging; 3.2.1 Effect of Variogram Parameters on Kriging; 3.2.2 Lognormal and Trans-Gaussian Kriging; 3.2.3 Cokriging; 3.2.4 Some Final Remarks; 3.3 Robust Kriging; 3.4 Universal Kriging; 3.4.1 Universal Kriging of Coal-Ash Data; 3.4.2 Trend-Surface Prediction; 3.4.3 Estimating the Variogram for Universal Kriging; 3.4.4 Bayesian Kriging; 3.4.5 Kriging Revisited; 3.5 Median-Polish Kriging; 3.5.1 Gridded Data; 3.5.2 Nongridded Data; 3.5.3 Median Polishing Spatial Data: Inference Results.
- 3.5.4 Median-Based Covariogram Estimators are Less Biased3.6 Geostatistical Data Simulated and Real; 3.6.1 Simulation of Spatial Processes; 3.6.2 Conditional Simulation; 3.6.3 Geostatistical Data; 4. Applications of Geostatistics; 4.1 Wolfcamp-Aquifer Data; 4.1.1 Intrinsic-Stationarity Assumption; 4.1.2 Nonconstant-Mean Assumption; 4.2 Soil-Water Tension Data; 4.3 Soil-Water-Infiltration Data; 4.3.1 Estimating and Modeling the Spatial Dependence; 4.3.2 Inference on Mean Effects (Spatial Analysis of Variance); 4.4 Sudden-Infant-Death-Syndrome Data; 4.5 Wheat-Yield Data.
- 4.5.1 Presence of Trend in the Data4.5.2 Intrinsic Stationarity; 4.5.3 Median-Polish (Robust) Kriging; 4.6 Acid-Deposition Data; 4.6.1 Spatial Modeling and Prediction; 4.6.2 Sampling Design; 4.7 Space-Time Geostatistical Data; 5. Special Topics in Statistics for Spatial Data; 5.1 Nonlinear Geostatistics; 5.2 Change of Support; 5.3 Stability of the Geostatistical Method; 5.3.1 Estimation of Spatial-Dependence Parameters; 5.3.2 Stability of the Kriging Predictor; 5.3.3 Stability of the Kriging Variance; 5.4 Intrinsic Random Functions of Order k.