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150618s2015 nju ob 001 0 eng |
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|a 519.5/35
|2 23
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|a UAMI
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|a Cressie, Noel A. C.
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|a Statistics for spatial data /
|c Noel A.C. Cressie.
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|a Revised edition.
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|a Hoboken, NJ :
|b John Wiley & Sons, Inc.,
|c 2015.
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|a 1 online resource
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Includes bibliographical references and indexes.
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|a Print version record and CIP data provided by publisher.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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650 |
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0 |
|a Spatial analysis (Statistics)
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650 |
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6 |
|a Analyse spatiale (Statistique)
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650 |
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7 |
|a spatial analysis.
|2 aat
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650 |
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|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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|a Spatial analysis (Statistics)
|2 fast
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|i has work:
|a Statistics for spatial data (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGM4rQGMbCF3GXcybBMbbb
|4 https://id.oclc.org/worldcat/ontology/hasWork
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776 |
0 |
8 |
|i Print version:
|a Cressie, Noel A.C.
|t Statistics for spatial data.
|b Revised edition.
|d Hoboken, NJ : John Wiley & Sons, Inc., 2015
|z 9781119114611
|w (DLC) 2015023985
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856 |
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|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=2008055
|z Texto completo
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