Info
Geostatistical functional data analysis / / edited by Jorge Mateu, Ramon Giraldo
| Geostatistical functional data analysis / / edited by Jorge Mateu, Ramon Giraldo |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (451 pages) : illustrations |
| Disciplina | 551.072/7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Functional analysis
Kriging Spatial analysis (Statistics) Geology - Statistical methods |
| ISBN |
1-119-38790-6
1-119-38791-4 1-119-38788-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword -- Chapter 1 Introduction to Geostatistical Functional Data Analysis -- 1.1 Spatial Statistics -- 1.2 Spatial Geostatistics -- 1.2.1 Regionalized Variables -- 1.2.2 Random Functions -- 1.2.3 Stationarity and Intrinsic Hypothesis -- 1.3 Spatiotemporal Geostatistics -- 1.3.1 Relevant Spatiotemporal Concepts -- 1.3.2 Spatiotemporal Kriging -- 1.3.3 Spatiotemporal Covariance Models -- 1.4 Functional Data Analysis in Brief -- References -- Part I Mathematical and Statistical Foundations -- Chapter 2 Mathematical Foundations of Functional Kriging in Hilbert Spaces and Riemannian Manifolds -- 2.1 Introduction -- 2.2 Definitions and Assumptions -- 2.3 Kriging Prediction in Hilbert Space: A Trace Approach -- 2.3.1 Ordinary and Universal Kriging in Hilbert Spaces -- 2.3.2 Estimating the Drift -- 2.3.3 An Example: Trace‐Variogram in Sobolev Spaces -- 2.3.4 An Application to Nonstationary Prediction of Temperatures Profiles -- 2.4 An Operatorial Viewpoint to Kriging -- 2.5 Kriging for Manifold‐Valued Random Fields -- 2.5.1 Residual Kriging -- 2.5.2 An Application to Positive Definite Matrices -- 2.5.3 Validity of the Local Tangent Space Approximation -- 2.6 Conclusion and Further Research -- References -- Chapter 3 Universal, Residual, and External Drift Functional Kriging -- 3.1 Introduction -- 3.2 Universal Kriging for Functional Data (UKFD) -- 3.3 Residual Kriging for Functional Data (ResKFD) -- 3.4 Functional Kriging with External Drift (FKED) -- 3.5 Accounting for Spatial Dependence in Drift Estimation -- 3.5.1 Drift Selection -- 3.6 Uncertainty Evaluation -- 3.7 Implementation Details in R -- 3.7.1 Example: Air Pollution Data -- 3.8 Conclusions -- References.
Chapter 4 Extending Functional Kriging When Data Are Multivariate Curves: Some Technical Considerations and Operational Solutions -- 4.1 Introduction -- 4.2 Principal Component Analysis for Curves -- 4.2.1 Karhunen-Loève Decomposition -- 4.2.2 Dealing with a Sample -- 4.3 Functional Kriging in a Nutshell -- 4.3.1 Solution Based on Basis Functions -- 4.3.2 Estimation of Spatial Covariances -- 4.4 An Example with the Precipitation Observations -- 4.4.1 Fitting Variogram Model -- 4.4.2 Making Prediction -- 4.5 Functional Principal Component Kriging -- 4.6 Multivariate Kriging with Functional Data -- 4.6.1 Multivariate FPCA -- 4.6.2 MFPCA Displays -- 4.6.3 Multivariate Functional Principal Component Kriging -- 4.6.4 Mixing Temperature and Precipitation Curves -- 4.7 Discussion -- 4.A.1 Computation of the Kriging Variance -- References -- Chapter 5 Geostatistical Analysis in Bayes Spaces: Probability Densities and Compositional Data -- 5.1 Introduction and Motivations -- 5.2 Bayes Hilbert Spaces: Natural Spaces for Functional Compositions -- 5.3 A Motivating Case Study: Particle‐Size Data in Heterogeneous Aquifers - Data Description -- 5.4 Kriging Stationary Functional Compositions -- 5.4.1 Model Description -- 5.4.2 Data Preprocessing -- 5.4.3 An Example of Application -- 5.4.4 Uncertainty Assessment -- 5.5 Analyzing Nonstationary Fields of FCs -- 5.6 Conclusions and Perspectives -- References -- Chapter 6 Spatial Functional Data Analysis for Probability Density Functions: Compositional Functional Data vs. Distributional Data Approach -- 6.1 FDA and SDA When Data Are Densities -- 6.1.1 Features of Density Functions as Compositional Functional Data -- 6.1.2 Features of Density Functions as Distributional Data -- 6.2 Measures of Spatial Association for Georeferenced Density Functions. 6.2.1 Identification of Spatial Clusters by Spatial Association Measures for Density Functions -- 6.3 Real Data Analysis -- 6.3.1 The SDA Distributional Approach -- 6.3.2 The Compositional-Functional Approach -- 6.3.3 Discussion -- 6.4 Conclusion -- Acknowledgments -- References -- Part II Statistical Techniques for Spatially Correlated Functional Data -- Chapter 7 Clustering Spatial Functional Data -- 7.1 Introduction -- 7.2 Model‐Based Clustering for Spatial Functional Data -- 7.2.1 The Expectation-Maximization (EM) Algorithm -- 7.2.1.1 E Step -- 7.2.1.2 M Step -- 7.2.2 Model Selection -- 7.3 Descendant Hierarchical Classification (HC) Based on Centrality Methods -- 7.3.1 Methodology -- 7.4 Application -- 7.4.1 Model‐Based Clustering -- 7.4.2 Hierarchical Classification -- 7.5 Conclusion -- References -- Chapter 8 Nonparametric Statistical Analysis of Spatially Distributed Functional Data -- 8.1 Introduction -- 8.2 Large Sample Properties -- 8.2.1 Uniform Almost Complete Convergence -- 8.3 Prediction -- 8.4 Numerical Results -- 8.4.1 Bandwidth Selection Procedure -- 8.4.2 Simulation Study -- 8.5 Conclusion -- 8.A.1 Some Preliminary Results for the Proofs -- 8.A.2 Proofs -- 8.A.2.1 Proof of Theorem 8.1 -- 8.A.2.2 Proof of Lemma A.3 -- 8.A.2.3 Proof of Lemma A.4 -- 8.A.2.4 Proof of Lemma A.5 -- 8.A.2.5 Proof of Lemma A.6 -- 8.A.2.6 Proof of Theorem 8.2 -- References -- Chapter 9 A Nonparametric Algorithm for Spatially Dependent Functional Data: Bagging Voronoi for Clustering, Dimensional Reduction, and Regression -- 9.1 Introduction -- 9.2 The Motivating Application -- 9.2.1 Data Preprocessing -- 9.3 The Bagging Voronoi Strategy -- 9.4 Bagging Voronoi Clustering (BVClu) -- 9.4.1 BVClu of the Telecom Data -- 9.4.1.1 Setting the BVClu Parameters -- 9.4.1.2 Results -- 9.5 Bagging Voronoi Dimensional Reduction (BVDim) -- 9.5.1 BVDim of the Telecom Data. 9.5.1.1 Setting the BVDim Parameters -- 9.5.1.2 Results -- 9.6 Bagging Voronoi Regression (BVReg) -- 9.6.1 Covariate Information: The DUSAF Data -- 9.6.2 BVReg of the Telecom Data -- 9.6.2.1 Setting the BVReg Parameters -- 9.6.2.2 Results -- 9.7 Conclusions and Discussion -- References -- Chapter 10 Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data -- 10.1 Introduction -- 10.2 Methodology -- 10.2.1 Comparing Means of Two Functional Populations -- 10.2.2 Extensions -- 10.2.2.1 Multiway FANOVA -- 10.3 Data Analysis -- 10.4 Conclusion and Future Works -- References -- Chapter 11 Modeling Spatially Dependent Functional Data by Spatial Regression with Differential Regularization -- 11.1 Introduction -- 11.2 Spatial Regression with Differential Regularization for Geostatistical Functional Data -- 11.2.1 A Separable Spatiotemporal Basis System -- 11.2.2 Discretization of the Penalized Sum‐of‐Square Error Functional -- 11.2.3 Properties of the Estimators -- 11.2.4 Model Without Covariates -- 11.2.5 An Alternative Formulation of the Model -- 11.3 Simulation Studies -- 11.4 An Illustrative Example: Study of the Waste Production in Venice Province -- 11.4.1 The Venice Waste Dataset -- 11.4.2 Analysis of Venice Waste Data by Spatial Regression with Differential Regularization -- 11.5 Model Extensions -- References -- Chapter 12 Quasi‐maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models -- 12.1 Introduction -- 12.2 Model -- 12.2.1 Truncated Conditional Likelihood Method -- 12.3 Results and Assumptions -- 12.4 Numerical Experiments -- 12.4.1 Monte Carlo Simulations -- 12.4.2 Real Data Application -- 12.5 Conclusion -- References -- Chapter 13 Spatial Prediction and Optimal Sampling for Multivariate Functional Random Fields -- 13.1 Background. 13.1.1 Multivariate Spatial Functional Random Fields -- 13.1.2 Functional Principal Components -- 13.1.3 The Spatial Random Field of Scores -- 13.2 Functional Kriging -- 13.2.1 Ordinary Functional Kriging (OFK) -- 13.2.2 Functional Kriging Using Scalar Simple Kriging of the Scores (FKSK) -- 13.2.3 Functional Kriging Using Scalar Simple Cokriging of the Scores (FKCK) -- 13.3 Functional Cokriging -- 13.3.1 Cokriging with Two Functional Random Fields -- 13.3.2 Cokriging with P Functional Random Fields -- 13.4 Optimal Sampling Designs for Spatial Prediction of Functional Data -- 13.4.1 Optimal Spatial Sampling for OFK -- 13.4.2 Optimal Spatial Sampling for FKSK -- 13.4.3 Optimal Spatial Sampling for FKCK -- 13.4.4 Optimal Spatial Sampling for Functional Cokriging -- 13.5 Real Data Analysis -- 13.6 Discussion and Conclusions -- References -- Part III Spatio-Temporal Functional Data -- Chapter 14 Spatio-temporal Functional Data Analysis -- 14.1 Introduction -- 14.2 Randomness Test -- 14.3 Change‐Point Test -- 14.4 Separability Tests -- 14.5 Trend Tests -- 14.6 Spatio-Temporal Extremes -- References -- Chapter 15 A Comparison of Spatiotemporal and Functional Kriging Approaches -- 15.1 Introduction -- 15.2 Preliminaries -- 15.3 Kriging -- 15.3.1 Functional Kriging -- 15.3.1.1 Ordinary Kriging for Functional Data -- 15.3.1.2 Pointwise Functional Kriging -- 15.3.1.3 Functional Kriging Total Model -- 15.3.2 Spatiotemporal Kriging -- 15.3.3 Evaluation of Kriging Methods -- 15.4 A Simulation Study -- 15.4.1 Separable -- 15.4.2 Non‐separable -- 15.4.3 Nonstationary -- 15.5 Application: Spatial Prediction of Temperature Curves in the Maritime Provinces of Canada -- 15.6 Concluding Remarks -- References -- Chapter 16 From Spatiotemporal Smoothing to Functional Spatial Regression: a Penalized Approach -- 16.1 Introduction. 16.2 Smoothing Spatial Data via Penalized Regression. |
| Record Nr. | UNINA-9910555245103321 |
| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Geostatistical functional data analysis / / edited by Jorge Mateu, Ramon Giraldo
| Geostatistical functional data analysis / / edited by Jorge Mateu, Ramon Giraldo |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (451 pages) : illustrations |
| Disciplina | 551.072/7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Functional analysis
Kriging Spatial analysis (Statistics) Geology - Statistical methods |
| ISBN |
1-119-38790-6
1-119-38791-4 1-119-38788-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword -- Chapter 1 Introduction to Geostatistical Functional Data Analysis -- 1.1 Spatial Statistics -- 1.2 Spatial Geostatistics -- 1.2.1 Regionalized Variables -- 1.2.2 Random Functions -- 1.2.3 Stationarity and Intrinsic Hypothesis -- 1.3 Spatiotemporal Geostatistics -- 1.3.1 Relevant Spatiotemporal Concepts -- 1.3.2 Spatiotemporal Kriging -- 1.3.3 Spatiotemporal Covariance Models -- 1.4 Functional Data Analysis in Brief -- References -- Part I Mathematical and Statistical Foundations -- Chapter 2 Mathematical Foundations of Functional Kriging in Hilbert Spaces and Riemannian Manifolds -- 2.1 Introduction -- 2.2 Definitions and Assumptions -- 2.3 Kriging Prediction in Hilbert Space: A Trace Approach -- 2.3.1 Ordinary and Universal Kriging in Hilbert Spaces -- 2.3.2 Estimating the Drift -- 2.3.3 An Example: Trace‐Variogram in Sobolev Spaces -- 2.3.4 An Application to Nonstationary Prediction of Temperatures Profiles -- 2.4 An Operatorial Viewpoint to Kriging -- 2.5 Kriging for Manifold‐Valued Random Fields -- 2.5.1 Residual Kriging -- 2.5.2 An Application to Positive Definite Matrices -- 2.5.3 Validity of the Local Tangent Space Approximation -- 2.6 Conclusion and Further Research -- References -- Chapter 3 Universal, Residual, and External Drift Functional Kriging -- 3.1 Introduction -- 3.2 Universal Kriging for Functional Data (UKFD) -- 3.3 Residual Kriging for Functional Data (ResKFD) -- 3.4 Functional Kriging with External Drift (FKED) -- 3.5 Accounting for Spatial Dependence in Drift Estimation -- 3.5.1 Drift Selection -- 3.6 Uncertainty Evaluation -- 3.7 Implementation Details in R -- 3.7.1 Example: Air Pollution Data -- 3.8 Conclusions -- References.
Chapter 4 Extending Functional Kriging When Data Are Multivariate Curves: Some Technical Considerations and Operational Solutions -- 4.1 Introduction -- 4.2 Principal Component Analysis for Curves -- 4.2.1 Karhunen-Loève Decomposition -- 4.2.2 Dealing with a Sample -- 4.3 Functional Kriging in a Nutshell -- 4.3.1 Solution Based on Basis Functions -- 4.3.2 Estimation of Spatial Covariances -- 4.4 An Example with the Precipitation Observations -- 4.4.1 Fitting Variogram Model -- 4.4.2 Making Prediction -- 4.5 Functional Principal Component Kriging -- 4.6 Multivariate Kriging with Functional Data -- 4.6.1 Multivariate FPCA -- 4.6.2 MFPCA Displays -- 4.6.3 Multivariate Functional Principal Component Kriging -- 4.6.4 Mixing Temperature and Precipitation Curves -- 4.7 Discussion -- 4.A.1 Computation of the Kriging Variance -- References -- Chapter 5 Geostatistical Analysis in Bayes Spaces: Probability Densities and Compositional Data -- 5.1 Introduction and Motivations -- 5.2 Bayes Hilbert Spaces: Natural Spaces for Functional Compositions -- 5.3 A Motivating Case Study: Particle‐Size Data in Heterogeneous Aquifers - Data Description -- 5.4 Kriging Stationary Functional Compositions -- 5.4.1 Model Description -- 5.4.2 Data Preprocessing -- 5.4.3 An Example of Application -- 5.4.4 Uncertainty Assessment -- 5.5 Analyzing Nonstationary Fields of FCs -- 5.6 Conclusions and Perspectives -- References -- Chapter 6 Spatial Functional Data Analysis for Probability Density Functions: Compositional Functional Data vs. Distributional Data Approach -- 6.1 FDA and SDA When Data Are Densities -- 6.1.1 Features of Density Functions as Compositional Functional Data -- 6.1.2 Features of Density Functions as Distributional Data -- 6.2 Measures of Spatial Association for Georeferenced Density Functions. 6.2.1 Identification of Spatial Clusters by Spatial Association Measures for Density Functions -- 6.3 Real Data Analysis -- 6.3.1 The SDA Distributional Approach -- 6.3.2 The Compositional-Functional Approach -- 6.3.3 Discussion -- 6.4 Conclusion -- Acknowledgments -- References -- Part II Statistical Techniques for Spatially Correlated Functional Data -- Chapter 7 Clustering Spatial Functional Data -- 7.1 Introduction -- 7.2 Model‐Based Clustering for Spatial Functional Data -- 7.2.1 The Expectation-Maximization (EM) Algorithm -- 7.2.1.1 E Step -- 7.2.1.2 M Step -- 7.2.2 Model Selection -- 7.3 Descendant Hierarchical Classification (HC) Based on Centrality Methods -- 7.3.1 Methodology -- 7.4 Application -- 7.4.1 Model‐Based Clustering -- 7.4.2 Hierarchical Classification -- 7.5 Conclusion -- References -- Chapter 8 Nonparametric Statistical Analysis of Spatially Distributed Functional Data -- 8.1 Introduction -- 8.2 Large Sample Properties -- 8.2.1 Uniform Almost Complete Convergence -- 8.3 Prediction -- 8.4 Numerical Results -- 8.4.1 Bandwidth Selection Procedure -- 8.4.2 Simulation Study -- 8.5 Conclusion -- 8.A.1 Some Preliminary Results for the Proofs -- 8.A.2 Proofs -- 8.A.2.1 Proof of Theorem 8.1 -- 8.A.2.2 Proof of Lemma A.3 -- 8.A.2.3 Proof of Lemma A.4 -- 8.A.2.4 Proof of Lemma A.5 -- 8.A.2.5 Proof of Lemma A.6 -- 8.A.2.6 Proof of Theorem 8.2 -- References -- Chapter 9 A Nonparametric Algorithm for Spatially Dependent Functional Data: Bagging Voronoi for Clustering, Dimensional Reduction, and Regression -- 9.1 Introduction -- 9.2 The Motivating Application -- 9.2.1 Data Preprocessing -- 9.3 The Bagging Voronoi Strategy -- 9.4 Bagging Voronoi Clustering (BVClu) -- 9.4.1 BVClu of the Telecom Data -- 9.4.1.1 Setting the BVClu Parameters -- 9.4.1.2 Results -- 9.5 Bagging Voronoi Dimensional Reduction (BVDim) -- 9.5.1 BVDim of the Telecom Data. 9.5.1.1 Setting the BVDim Parameters -- 9.5.1.2 Results -- 9.6 Bagging Voronoi Regression (BVReg) -- 9.6.1 Covariate Information: The DUSAF Data -- 9.6.2 BVReg of the Telecom Data -- 9.6.2.1 Setting the BVReg Parameters -- 9.6.2.2 Results -- 9.7 Conclusions and Discussion -- References -- Chapter 10 Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data -- 10.1 Introduction -- 10.2 Methodology -- 10.2.1 Comparing Means of Two Functional Populations -- 10.2.2 Extensions -- 10.2.2.1 Multiway FANOVA -- 10.3 Data Analysis -- 10.4 Conclusion and Future Works -- References -- Chapter 11 Modeling Spatially Dependent Functional Data by Spatial Regression with Differential Regularization -- 11.1 Introduction -- 11.2 Spatial Regression with Differential Regularization for Geostatistical Functional Data -- 11.2.1 A Separable Spatiotemporal Basis System -- 11.2.2 Discretization of the Penalized Sum‐of‐Square Error Functional -- 11.2.3 Properties of the Estimators -- 11.2.4 Model Without Covariates -- 11.2.5 An Alternative Formulation of the Model -- 11.3 Simulation Studies -- 11.4 An Illustrative Example: Study of the Waste Production in Venice Province -- 11.4.1 The Venice Waste Dataset -- 11.4.2 Analysis of Venice Waste Data by Spatial Regression with Differential Regularization -- 11.5 Model Extensions -- References -- Chapter 12 Quasi‐maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models -- 12.1 Introduction -- 12.2 Model -- 12.2.1 Truncated Conditional Likelihood Method -- 12.3 Results and Assumptions -- 12.4 Numerical Experiments -- 12.4.1 Monte Carlo Simulations -- 12.4.2 Real Data Application -- 12.5 Conclusion -- References -- Chapter 13 Spatial Prediction and Optimal Sampling for Multivariate Functional Random Fields -- 13.1 Background. 13.1.1 Multivariate Spatial Functional Random Fields -- 13.1.2 Functional Principal Components -- 13.1.3 The Spatial Random Field of Scores -- 13.2 Functional Kriging -- 13.2.1 Ordinary Functional Kriging (OFK) -- 13.2.2 Functional Kriging Using Scalar Simple Kriging of the Scores (FKSK) -- 13.2.3 Functional Kriging Using Scalar Simple Cokriging of the Scores (FKCK) -- 13.3 Functional Cokriging -- 13.3.1 Cokriging with Two Functional Random Fields -- 13.3.2 Cokriging with P Functional Random Fields -- 13.4 Optimal Sampling Designs for Spatial Prediction of Functional Data -- 13.4.1 Optimal Spatial Sampling for OFK -- 13.4.2 Optimal Spatial Sampling for FKSK -- 13.4.3 Optimal Spatial Sampling for FKCK -- 13.4.4 Optimal Spatial Sampling for Functional Cokriging -- 13.5 Real Data Analysis -- 13.6 Discussion and Conclusions -- References -- Part III Spatio-Temporal Functional Data -- Chapter 14 Spatio-temporal Functional Data Analysis -- 14.1 Introduction -- 14.2 Randomness Test -- 14.3 Change‐Point Test -- 14.4 Separability Tests -- 14.5 Trend Tests -- 14.6 Spatio-Temporal Extremes -- References -- Chapter 15 A Comparison of Spatiotemporal and Functional Kriging Approaches -- 15.1 Introduction -- 15.2 Preliminaries -- 15.3 Kriging -- 15.3.1 Functional Kriging -- 15.3.1.1 Ordinary Kriging for Functional Data -- 15.3.1.2 Pointwise Functional Kriging -- 15.3.1.3 Functional Kriging Total Model -- 15.3.2 Spatiotemporal Kriging -- 15.3.3 Evaluation of Kriging Methods -- 15.4 A Simulation Study -- 15.4.1 Separable -- 15.4.2 Non‐separable -- 15.4.3 Nonstationary -- 15.5 Application: Spatial Prediction of Temperature Curves in the Maritime Provinces of Canada -- 15.6 Concluding Remarks -- References -- Chapter 16 From Spatiotemporal Smoothing to Functional Spatial Regression: a Penalized Approach -- 16.1 Introduction. 16.2 Smoothing Spatial Data via Penalized Regression. |
| Record Nr. | UNINA-9910830501003321 |
| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatial and spatio-temporal geostatistical modeling and kriging / / José-María Montero, Department of Statistics, University of Castilla-La Mancha, Spain, Gema Fernández-Aviles, Department of Statistics, University of Castilla-La Mancha, Spain, Jorge Mateu, Department of Mathematics, University Jaume I of Castellon, Spain
| Spatial and spatio-temporal geostatistical modeling and kriging / / José-María Montero, Department of Statistics, University of Castilla-La Mancha, Spain, Gema Fernández-Aviles, Department of Statistics, University of Castilla-La Mancha, Spain, Jorge Mateu, Department of Mathematics, University Jaume I of Castellon, Spain |
| Autore | Montero José María |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015 |
| Descrizione fisica | 1 online resource |
| Disciplina | 551.01/5195 |
| Altri autori (Persone) |
Fernández-AvilésGema
MateuJorge |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Geology - Statistical methods
Kriging |
| ISBN |
1-118-76238-X
1-118-76243-6 1-118-76242-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Dedication -- Contents -- Foreword by Abdel H. El-Shaarawi -- Foreword by Hao Zhang -- List of figures -- List of tables -- About the companion website -- Chapter 1 From classical statistics to geostatistics -- 1.1 Not all spatial data are geostatistical data -- 1.2 The limits of classical statistics -- 1.3 A real geostatistical dataset: data on carbon monoxide in Madrid, Spain -- Chapter 2 Geostatistics: preliminaries -- 2.1 Regionalized variables -- 2.2 Random functions -- 2.3 Stationary and intrinsic hypotheses -- 2.3.1 Stationarity -- 2.3.2 Stationary random functions in the strict sense -- 2.3.3 Second-order stationary random functions -- 2.3.4 Intrinsically stationary random functions -- 2.3.5 Non-stationary random functions -- 2.4 Support -- Chapter 3 Structural analysis -- 3.1 Introduction -- 3.2 Covariance function -- 3.2.1 Definition and properties -- 3.2.2 Some theoretical isotropic covariance functions -- 3.3 Empirical covariogram -- 3.4 Semivariogram -- 3.4.1 Definition and properties -- 3.4.2 Behavior at intermediate and large distances -- 3.4.3 Behavior near the origin -- 3.4.4 A discontinuity at the origin -- 3.5 Theoretical semivariogram models -- 3.5.1 Semivariograms with a sill -- 3.5.2 Semivariograms with a hole effect -- 3.5.3 Semivariograms without a sill -- 3.5.4 Combining semivariogram models -- 3.6 Empirical semivariogram -- 3.7 Anisotropy -- 3.8 Fitting a semivariogram model -- 3.8.1 Manual fitting -- 3.8.2 Automatic fitting -- Chapter 4 Spatial prediction and kriging -- 4.1 Introduction -- 4.2 Neighborhood -- 4.3 Ordinary kriging -- 4.3.1 Point observation support and point predictor -- 4.3.2 Effects of a change in the model parameters -- 4.3.3 Point observation support and block predictor -- 4.3.4 Block observation support and block predictor.
4.4 Simple kriging: the special case of known mean -- 4.5 Simple kriging with an estimated mean -- 4.6 Universal kriging -- 4.6.1 Point observation support and point predictor -- 4.6.2 Point observation support and block predictor -- 4.6.3 Block observation support and block predictor -- 4.6.4 Kriging and exact interpolation -- 4.7 Residual kriging -- 4.7.1 Direct residual kriging -- 4.7.2 Iterative residual kriging -- 4.7.3 Modified iterative residual kriging -- 4.8 Median-Polish kriging -- 4.9 Cross-validation -- 4.10 Non-linear kriging -- 4.10.1 Disjunctive kriging -- 4.10.2 Indicator kriging -- Chapter 5 Geostatistics and spatio-temporal random functions -- 5.1 Spatio-temporal geostatistics -- 5.2 Spatio-temporal continuity -- 5.3 Relevant spatio-temporal concepts -- 5.4 Properties of the spatio-temporal covariance and semivariogram -- Chapter 6 Spatio-temporal structural analysis (I): empirical semivariogram and covariogram estimation and model fitting -- 6.1 Introduction -- 6.2 The empirical spatio-temporal semivariogram and covariogram -- 6.3 Fitting spatio-temporal semivariogram and covariogram models -- 6.4 Validation and comparison of spatio-temporal semivariogram and covariogram models -- Chapter 7 Spatio-temporal structural analysis (II): theoretical covariance models -- 7.1 Introduction -- 7.2 Combined distance or metric model -- 7.3 Sum model -- 7.4 Combined metric-sum model -- 7.5 Product model -- 7.6 Product-sum model -- 7.7 Porcu and Mateu mixture-based models -- 7.8 General product-sum model -- 7.9 Integrated product and product-sum models -- 7.10 Models proposed by Cressie and Huang -- 7.11 Models proposed by Gneiting -- 7.12 Mixture models proposed by Ma -- 7.12.1 Covariance functions generated by scale mixtures -- 7.12.2 Covariance functions generated by positive power mixtures. 7.13 Models generated by linear combinations proposed by Ma -- 7.14 Models proposed by Stein -- 7.15 Construction of covariance functions using copulas and completely monotonic functions -- 7.16 Generalized product-sum model -- 7.17 Models that are not fully symmetric -- 7.18 Mixture-based Bernstein zonally anisotropic covariance functions -- 7.19 Non-stationary models -- 7.19.1 Mixture of locally orthogonal stationary processes -- 7.19.2 Non-stationary models proposed by Ma -- 7.19.3 Non-stationary models proposed by Porcu and Mateu -- 7.20 Anisotropic covariance functions by Porcu and Mateu -- 7.20.1 Constructing temporally symmetric and spatially anisotropic covariance functions -- 7.20.2 Generalizing the class of spatio-temporal covariance functions proposed by Gneiting -- 7.20.3 Differentiation and integration operators acting on classes of anisotropic covariance functions on the basis of isotropic components: 'La descente étendue' -- 7.21 Spatio-temporal constructions based on quasi-arithmetic means of covariance functions -- 7.21.1 Multivariate quasi-arithmetic compositions -- 7.21.2 Permissibility criteria for quasi-arithmetic means of covariance functions on Rd -- 7.21.3 The use of quasi-arithmetic functionals to build non-separable, stationary, spatio-temporal covariance functions -- 7.21.4 Quasi-arithmeticity and non-stationarity in space -- Chapter 8 Spatio-temporal prediction and kriging -- 8.1 Spatio-temporal kriging -- 8.2 Spatio-temporal kriging equations -- Chapter 9 An introduction to functional geostatistics -- 9.1 Functional data analysis -- 9.2 Functional geostatistics: The parametric vs. the non-parametric approach -- 9.3 Functional ordinary kriging -- 9.3.1 Preliminaries -- 9.3.2 Functional ordinary kriging equations -- 9.3.3 Estimating the trace-semivariogram -- 9.3.4 Functional cross-validation -- Appendices. Appendix A Spectral representations -- A.1 Spectral representation of the covariogram -- A.2 Spectral representation of the semivariogram -- Appendix B Probabilistic aspects of Uij=Z(si)-Z(sj) -- Appendix C Basic theory on restricted maximum likelihood -- C.1 Restricted Maximum Likelihood equation -- Appendix D Most relevant proofs -- D.1 Product model: Peculiarity (ii) (Rodríguez-Iturbe and Mejia 1974 -- De Cesare et al. 1997) -- D.2 Product model: Peculiarity (iv) (Rodríguez-Iturbe and Mejia 1974 -- De Cesare et al. 1997) -- D.3 Product-sum model: Semivariogram expression (7.29) (De Iaco et al. 2001) -- D.4 General product-sum model: Obtaining the constant k (De Iaco et al. 2001) -- D.5 General product-sum model: Theorem 7.8.1 (De Iaco et al. 2001) -- D.6 General product-sum model: Theorem 7.8.2. (De Iaco et al. 2001) -- D.7 Generalized product-sum model. Proposition 1 1 (Gregori et al. 2008) -- D.8 Generalized product-sum model. Proposition 1 2 for n = 2 (Gregori et al. 2008) -- D.9 Generalized product-sum model. Corollary 1 3 of Proposition 2 (Gregori et al. 2008) -- D.10 Generalized product-sum model. Range of θ. Case 1: The Gaussian case 4 (Gregori et al. 2008) -- D.11 Generalized product-sum model. Range of θ. Case 2: The Matérn case 5 (Gregori et al. 2008) -- D.12 Generalized product-sum model. Range of θ. Case 3: The Gaussian-Matérn case 6 (Gregori et al. 2008) -- D.13 Mixture-based Bernstein zonally anisotropic covariance functions. Theorem 7.18.1 (Ma 2003b) -- D.14 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.159) (Ma 2003c). D.15 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.161) is a valid covariance function (Ma 2003c) -- D.16 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.163) Ma (2003c) -- D.17 Permissibility criteria for quasi-arithmetic means of covariance functions. Proposition 1 (Porcu et al. 2009b) -- Bibliography and further reading -- Index -- Supplemental Images -- Wiley Series in Probability and Statistics -- EULA. |
| Record Nr. | UNINA-9910208951203321 |
Montero José María
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| Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatial and spatio-temporal geostatistical modeling and kriging / / José-María Montero, Department of Statistics, University of Castilla-La Mancha, Spain, Gema Fernández-Aviles, Department of Statistics, University of Castilla-La Mancha, Spain, Jorge Mateu, Department of Mathematics, University Jaume I of Castellon, Spain
| Spatial and spatio-temporal geostatistical modeling and kriging / / José-María Montero, Department of Statistics, University of Castilla-La Mancha, Spain, Gema Fernández-Aviles, Department of Statistics, University of Castilla-La Mancha, Spain, Jorge Mateu, Department of Mathematics, University Jaume I of Castellon, Spain |
| Autore | Montero José María |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015 |
| Descrizione fisica | 1 online resource |
| Disciplina | 551.01/5195 |
| Altri autori (Persone) |
Fernández-AvilésGema
MateuJorge |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Geology - Statistical methods
Kriging |
| ISBN |
1-118-76238-X
1-118-76243-6 1-118-76242-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Dedication -- Contents -- Foreword by Abdel H. El-Shaarawi -- Foreword by Hao Zhang -- List of figures -- List of tables -- About the companion website -- Chapter 1 From classical statistics to geostatistics -- 1.1 Not all spatial data are geostatistical data -- 1.2 The limits of classical statistics -- 1.3 A real geostatistical dataset: data on carbon monoxide in Madrid, Spain -- Chapter 2 Geostatistics: preliminaries -- 2.1 Regionalized variables -- 2.2 Random functions -- 2.3 Stationary and intrinsic hypotheses -- 2.3.1 Stationarity -- 2.3.2 Stationary random functions in the strict sense -- 2.3.3 Second-order stationary random functions -- 2.3.4 Intrinsically stationary random functions -- 2.3.5 Non-stationary random functions -- 2.4 Support -- Chapter 3 Structural analysis -- 3.1 Introduction -- 3.2 Covariance function -- 3.2.1 Definition and properties -- 3.2.2 Some theoretical isotropic covariance functions -- 3.3 Empirical covariogram -- 3.4 Semivariogram -- 3.4.1 Definition and properties -- 3.4.2 Behavior at intermediate and large distances -- 3.4.3 Behavior near the origin -- 3.4.4 A discontinuity at the origin -- 3.5 Theoretical semivariogram models -- 3.5.1 Semivariograms with a sill -- 3.5.2 Semivariograms with a hole effect -- 3.5.3 Semivariograms without a sill -- 3.5.4 Combining semivariogram models -- 3.6 Empirical semivariogram -- 3.7 Anisotropy -- 3.8 Fitting a semivariogram model -- 3.8.1 Manual fitting -- 3.8.2 Automatic fitting -- Chapter 4 Spatial prediction and kriging -- 4.1 Introduction -- 4.2 Neighborhood -- 4.3 Ordinary kriging -- 4.3.1 Point observation support and point predictor -- 4.3.2 Effects of a change in the model parameters -- 4.3.3 Point observation support and block predictor -- 4.3.4 Block observation support and block predictor.
4.4 Simple kriging: the special case of known mean -- 4.5 Simple kriging with an estimated mean -- 4.6 Universal kriging -- 4.6.1 Point observation support and point predictor -- 4.6.2 Point observation support and block predictor -- 4.6.3 Block observation support and block predictor -- 4.6.4 Kriging and exact interpolation -- 4.7 Residual kriging -- 4.7.1 Direct residual kriging -- 4.7.2 Iterative residual kriging -- 4.7.3 Modified iterative residual kriging -- 4.8 Median-Polish kriging -- 4.9 Cross-validation -- 4.10 Non-linear kriging -- 4.10.1 Disjunctive kriging -- 4.10.2 Indicator kriging -- Chapter 5 Geostatistics and spatio-temporal random functions -- 5.1 Spatio-temporal geostatistics -- 5.2 Spatio-temporal continuity -- 5.3 Relevant spatio-temporal concepts -- 5.4 Properties of the spatio-temporal covariance and semivariogram -- Chapter 6 Spatio-temporal structural analysis (I): empirical semivariogram and covariogram estimation and model fitting -- 6.1 Introduction -- 6.2 The empirical spatio-temporal semivariogram and covariogram -- 6.3 Fitting spatio-temporal semivariogram and covariogram models -- 6.4 Validation and comparison of spatio-temporal semivariogram and covariogram models -- Chapter 7 Spatio-temporal structural analysis (II): theoretical covariance models -- 7.1 Introduction -- 7.2 Combined distance or metric model -- 7.3 Sum model -- 7.4 Combined metric-sum model -- 7.5 Product model -- 7.6 Product-sum model -- 7.7 Porcu and Mateu mixture-based models -- 7.8 General product-sum model -- 7.9 Integrated product and product-sum models -- 7.10 Models proposed by Cressie and Huang -- 7.11 Models proposed by Gneiting -- 7.12 Mixture models proposed by Ma -- 7.12.1 Covariance functions generated by scale mixtures -- 7.12.2 Covariance functions generated by positive power mixtures. 7.13 Models generated by linear combinations proposed by Ma -- 7.14 Models proposed by Stein -- 7.15 Construction of covariance functions using copulas and completely monotonic functions -- 7.16 Generalized product-sum model -- 7.17 Models that are not fully symmetric -- 7.18 Mixture-based Bernstein zonally anisotropic covariance functions -- 7.19 Non-stationary models -- 7.19.1 Mixture of locally orthogonal stationary processes -- 7.19.2 Non-stationary models proposed by Ma -- 7.19.3 Non-stationary models proposed by Porcu and Mateu -- 7.20 Anisotropic covariance functions by Porcu and Mateu -- 7.20.1 Constructing temporally symmetric and spatially anisotropic covariance functions -- 7.20.2 Generalizing the class of spatio-temporal covariance functions proposed by Gneiting -- 7.20.3 Differentiation and integration operators acting on classes of anisotropic covariance functions on the basis of isotropic components: 'La descente étendue' -- 7.21 Spatio-temporal constructions based on quasi-arithmetic means of covariance functions -- 7.21.1 Multivariate quasi-arithmetic compositions -- 7.21.2 Permissibility criteria for quasi-arithmetic means of covariance functions on Rd -- 7.21.3 The use of quasi-arithmetic functionals to build non-separable, stationary, spatio-temporal covariance functions -- 7.21.4 Quasi-arithmeticity and non-stationarity in space -- Chapter 8 Spatio-temporal prediction and kriging -- 8.1 Spatio-temporal kriging -- 8.2 Spatio-temporal kriging equations -- Chapter 9 An introduction to functional geostatistics -- 9.1 Functional data analysis -- 9.2 Functional geostatistics: The parametric vs. the non-parametric approach -- 9.3 Functional ordinary kriging -- 9.3.1 Preliminaries -- 9.3.2 Functional ordinary kriging equations -- 9.3.3 Estimating the trace-semivariogram -- 9.3.4 Functional cross-validation -- Appendices. Appendix A Spectral representations -- A.1 Spectral representation of the covariogram -- A.2 Spectral representation of the semivariogram -- Appendix B Probabilistic aspects of Uij=Z(si)-Z(sj) -- Appendix C Basic theory on restricted maximum likelihood -- C.1 Restricted Maximum Likelihood equation -- Appendix D Most relevant proofs -- D.1 Product model: Peculiarity (ii) (Rodríguez-Iturbe and Mejia 1974 -- De Cesare et al. 1997) -- D.2 Product model: Peculiarity (iv) (Rodríguez-Iturbe and Mejia 1974 -- De Cesare et al. 1997) -- D.3 Product-sum model: Semivariogram expression (7.29) (De Iaco et al. 2001) -- D.4 General product-sum model: Obtaining the constant k (De Iaco et al. 2001) -- D.5 General product-sum model: Theorem 7.8.1 (De Iaco et al. 2001) -- D.6 General product-sum model: Theorem 7.8.2. (De Iaco et al. 2001) -- D.7 Generalized product-sum model. Proposition 1 1 (Gregori et al. 2008) -- D.8 Generalized product-sum model. Proposition 1 2 for n = 2 (Gregori et al. 2008) -- D.9 Generalized product-sum model. Corollary 1 3 of Proposition 2 (Gregori et al. 2008) -- D.10 Generalized product-sum model. Range of θ. Case 1: The Gaussian case 4 (Gregori et al. 2008) -- D.11 Generalized product-sum model. Range of θ. Case 2: The Matérn case 5 (Gregori et al. 2008) -- D.12 Generalized product-sum model. Range of θ. Case 3: The Gaussian-Matérn case 6 (Gregori et al. 2008) -- D.13 Mixture-based Bernstein zonally anisotropic covariance functions. Theorem 7.18.1 (Ma 2003b) -- D.14 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.159) (Ma 2003c). D.15 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.161) is a valid covariance function (Ma 2003c) -- D.16 Construction of non-stationary spatio-temporal covariance functions using spatio-temporal stationary covariances and intrinsically stationary semivariograms. Equation (7.163) Ma (2003c) -- D.17 Permissibility criteria for quasi-arithmetic means of covariance functions. Proposition 1 (Porcu et al. 2009b) -- Bibliography and further reading -- Index -- Supplemental Images -- Wiley Series in Probability and Statistics -- EULA. |
| Record Nr. | UNINA-9910806919403321 |
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Montero José María
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| Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatio-temporal design [[electronic resource] ] : advances in efficient data acquisition / / edited by Jorge Mateu, Werner G. Müller
| Spatio-temporal design [[electronic resource] ] : advances in efficient data acquisition / / edited by Jorge Mateu, Werner G. Müller |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2013 |
| Descrizione fisica | 1 online resource (379 p.) |
| Disciplina |
001.4/33
001.433 570.15195 |
| Altri autori (Persone) |
MateuJorge
MüllerW. G (Werner G.) |
| Collana |
Statistics in Practice
Statistics in practice |
| Soggetto topico |
Sampling (Statistics)
Spatial analysis (Statistics) |
| ISBN |
1-118-44186-9
1-299-18731-5 1-118-44189-3 1-118-44188-5 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Spatio-temporal design; Contents; Contributors; Foreword; Chapter 1 Collecting spatio-temporal data; 1.1 Introduction; 1.2 Paradigms in spatio-temporal design; 1.3 Paradigms in spatio-temporal modeling; 1.4 Geostatistics and spatio-temporal random functions; 1.4.1 Relevant spatio-temporal concepts; 1.4.2 Properties of the spatio-temporal covariance and variogram functions; 1.4.3 Spatio-temporal kriging; 1.4.4 Spatio-temporal covariance models; 1.4.5 Parametric estimation of spatio-temporal covariograms; 1.5 Types of design criteria and numerical optimization
1.6 The problem set: Upper Austria1.6.1 Climatic data; 1.6.2 Grassland usage; 1.7 The chapters; Acknowledgments; References; Chapter 2 Model-based frequentist design for univariate and multivariate geostatistics; 2.1 Introduction; 2.2 Design for univariate geostatistics; 2.2.1 Data-model framework; 2.2.2 Design criteria; 2.2.3 Algorithms; 2.2.4 Toy example; 2.3 Design for multivariate geostatistics; 2.3.1 Data-model framework; 2.3.2 Design criteria; 2.3.3 Toy example; 2.4 Application: Austrian precipitation data network; 2.5 Conclusions; References Chapter 3 Model-based criteria heuristics for second-phase spatial sampling3.1 Introduction; 3.2 Geometric and geostatistical designs; 3.2.1 Efficiency of spatial sampling designs; 3.2.2 Sampling spatial variables in a geostatistical context; 3.2.3 Sampling designs minimizing the kriging variance; 3.3 Augmented designs: Second-phase sampling; 3.3.1 Additional sampling schemes to maximize change in the kriging variance; 3.3.2 A weighted kriging variance approach; 3.4 A simulated annealing approach; 3.5 Illustration; 3.5.1 Initial sampling designs; 3.5.2 Augmented designs; 3.6 Discussion ReferencesChapter 4 Spatial sampling design by means of spectral approximations to the error process; 4.1 Introduction; 4.2 A brief review on spatial sampling design; 4.3 The spatial mixed linear model; 4.4 Classical Bayesian experimental design problem; 4.5 The Smith and Zhu design criterion; 4.6 Spatial sampling design for trans-Gaussian kriging; 4.7 The spatDesign toolbox; 4.7.1 Covariance estimation and variography software; 4.7.2 Spatial interpolation and kriging software; 4.7.3 Spatial sampling design software; 4.8 An example session; 4.8.1 Preparatory calculations 4.8.2 Optimal design for the BSLM4.8.3 Design for the trans-Gaussian kriging; 4.9 Conclusions; References; Chapter 5 Entropy-based network design using hierarchical Bayesian kriging; 5.1 Introduction; 5.2 Entropy-based network design using hierarchical Bayesian kriging; 5.3 The data; 5.4 Spatio-temporal modeling; 5.5 Obtaining a staircase data structure; 5.6 Estimating the hyperparameters Hg and the spatial correlations between gauge stations; 5.7 Spatial predictive distribution over the 445 areas located in the 18 districts of Upper Austria 5.8 Adding gauge stations over the 445 areas located in the 18 districts of Upper Austria |
| Record Nr. | UNINA-9910141502403321 |
| Chichester, West Sussex, U.K., : Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatio-temporal design : advances in efficient data acquisition / / edited by Jorge Mateu, Werner G. Muller
| Spatio-temporal design : advances in efficient data acquisition / / edited by Jorge Mateu, Werner G. Muller |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2013 |
| Descrizione fisica | 1 online resource (379 p.) |
| Disciplina | 001.4/33 |
| Altri autori (Persone) |
MateuJorge
MullerW. G (Werner G.) |
| Collana | Statistics in practice |
| Soggetto topico |
Sampling (Statistics)
Spatial analysis (Statistics) |
| ISBN |
9781118441862
1118441869 9781299187313 1299187315 9781118441893 1118441893 9781118441886 1118441885 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Spatio-temporal design; Contents; Contributors; Foreword; Chapter 1 Collecting spatio-temporal data; 1.1 Introduction; 1.2 Paradigms in spatio-temporal design; 1.3 Paradigms in spatio-temporal modeling; 1.4 Geostatistics and spatio-temporal random functions; 1.4.1 Relevant spatio-temporal concepts; 1.4.2 Properties of the spatio-temporal covariance and variogram functions; 1.4.3 Spatio-temporal kriging; 1.4.4 Spatio-temporal covariance models; 1.4.5 Parametric estimation of spatio-temporal covariograms; 1.5 Types of design criteria and numerical optimization
1.6 The problem set: Upper Austria1.6.1 Climatic data; 1.6.2 Grassland usage; 1.7 The chapters; Acknowledgments; References; Chapter 2 Model-based frequentist design for univariate and multivariate geostatistics; 2.1 Introduction; 2.2 Design for univariate geostatistics; 2.2.1 Data-model framework; 2.2.2 Design criteria; 2.2.3 Algorithms; 2.2.4 Toy example; 2.3 Design for multivariate geostatistics; 2.3.1 Data-model framework; 2.3.2 Design criteria; 2.3.3 Toy example; 2.4 Application: Austrian precipitation data network; 2.5 Conclusions; References Chapter 3 Model-based criteria heuristics for second-phase spatial sampling3.1 Introduction; 3.2 Geometric and geostatistical designs; 3.2.1 Efficiency of spatial sampling designs; 3.2.2 Sampling spatial variables in a geostatistical context; 3.2.3 Sampling designs minimizing the kriging variance; 3.3 Augmented designs: Second-phase sampling; 3.3.1 Additional sampling schemes to maximize change in the kriging variance; 3.3.2 A weighted kriging variance approach; 3.4 A simulated annealing approach; 3.5 Illustration; 3.5.1 Initial sampling designs; 3.5.2 Augmented designs; 3.6 Discussion ReferencesChapter 4 Spatial sampling design by means of spectral approximations to the error process; 4.1 Introduction; 4.2 A brief review on spatial sampling design; 4.3 The spatial mixed linear model; 4.4 Classical Bayesian experimental design problem; 4.5 The Smith and Zhu design criterion; 4.6 Spatial sampling design for trans-Gaussian kriging; 4.7 The spatDesign toolbox; 4.7.1 Covariance estimation and variography software; 4.7.2 Spatial interpolation and kriging software; 4.7.3 Spatial sampling design software; 4.8 An example session; 4.8.1 Preparatory calculations 4.8.2 Optimal design for the BSLM4.8.3 Design for the trans-Gaussian kriging; 4.9 Conclusions; References; Chapter 5 Entropy-based network design using hierarchical Bayesian kriging; 5.1 Introduction; 5.2 Entropy-based network design using hierarchical Bayesian kriging; 5.3 The data; 5.4 Spatio-temporal modeling; 5.5 Obtaining a staircase data structure; 5.6 Estimating the hyperparameters Hg and the spatial correlations between gauge stations; 5.7 Spatial predictive distribution over the 445 areas located in the 18 districts of Upper Austria 5.8 Adding gauge stations over the 445 areas located in the 18 districts of Upper Austria |
| Record Nr. | UNINA-9910825994603321 |
| Chichester, West Sussex, U.K., : Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||