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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 | ||
| ||