Geostatistical functional data analysis / edited by Jorge Mateu, Ramon Giraldo.
Contributor(s): Mateu, Jorge [editor.] | Giraldo, Ramon [editor.]
Language: English Series: Wiley series in probability and statistics: Publisher: Hoboken, NJ : Wiley, ©2021Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119387848; 9781119387916; 1119387914; 9781119387886; 1119387884; 9781119387909; 1119387906Subject(s): Geology -- Statistical methods | Kriging | Spatial analysis (Statistics) | Functional analysisGenre/Form: Electronic books.DDC classification: 551.072/7 LOC classification: QE33.2.S82Online resources: Full text is available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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| 551 P316 1970a Geology / | 551 P736 2007 Physical geology / | 551.03 L314 1990 Collins dictionary of geology/ | 551.072/7 Geostatistical functional data analysis / | 551.21 H224 1991 The mysteries of Taal: a Philippine volcano and lake, her sea life and lost towns/ | 551.21 In9 1993 In the shadow of the lingering Mt. Pinatubo disaster/ | 551.21 P217 2008 Fundamentals of physical volcanology / |
Includes bibliographical references and index.
Table of Contents
List of Contributors xiii
Foreword xvi
1 Introduction to Geostatistical Functional Data Analysis 1
Jorge Mateu and Ramón Giraldo
1.1 Spatial Statistics 1
1.2 Spatial Geostatistics 7
1.2.1 Regionalized Variables 7
1.2.2 Random Functions 7
1.2.3 Stationarity and Intrinsic Hypothesis 9
1.3 Spatiotemporal Geostatistics 12
1.3.1 Relevant Spatiotemporal Concepts 12
1.3.2 Spatiotemporal Kriging 16
1.3.3 Spatiotemporal Covariance Models 17
1.4 Functional Data Analysis in Brief 18
References 22
Part I Mathematical and Statistical Foundations 27
2 Mathematical Foundations of Functional Kriging in Hilbert Spaces and Riemannian Manifolds 29
Alessandra Menafoglio, Davide Pigoli, and Piercesare Secchi
2.1 Introduction 29
2.2 Definitions and Assumptions 30
2.3 Kriging Prediction in Hilbert Space: A Trace Approach 33
2.3.1 Ordinary and Universal Kriging in Hilbert Spaces 33
2.3.2 Estimating the Drift 36
2.3.3 An Example: Trace-Variogram in Sobolev Spaces 37
2.3.4 An Application to Nonstationary Prediction of Temperatures Profiles 39
2.4 An Operatorial Viewpoint to Kriging 42
2.5 Kriging for Manifold-Valued Random Fields 45
2.5.1 Residual Kriging 45
2.5.2 An Application to Positive Definite Matrices 47
2.5.3 Validity of the Local Tangent Space Approximation 49
2.6 Conclusion and Further Research 53
References 53
3 Universal, Residual, and External Drift Functional Kriging 55
Maria Franco-Villoria and Rosaria Ignaccolo
3.1 Introduction 56
3.2 Universal Kriging for Functional Data (UKFD) 56
3.3 Residual Kriging for Functional Data (ResKFD) 58
3.4 Functional Kriging with External Drift (FKED) 60
3.5 Accounting for Spatial Dependence in Drift Estimation 61
3.5.1 Drift Selection 62
3.6 Uncertainty Evaluation 62
3.7 Implementation Details in R 64
3.7.1 Example: Air Pollution Data 64
3.8 Conclusions 69
References 71
4 Extending Functional Kriging When Data Are Multivariate Curves: Some Technical Considerations and Operational Solutions 73
David Nerini, Claude Manté, and Pascal Monestiez
4.1 Introduction 73
4.2 Principal Component Analysis for Curves 74
4.2.1 Karhunen–Loève Decomposition 74
4.2.2 Dealing with a Sample 76
4.3 Functional Kriging in a Nutshell 78
4.3.1 Solution Based on Basis Functions 79
4.3.2 Estimation of Spatial Covariances 81
4.4 An Example with the Precipitation Observations 82
4.4.1 Fitting Variogram Model 83
4.4.2 Making Prediction 83
4.5 Functional Principal Component Kriging 85
4.6 Multivariate Kriging with Functional Data 88
4.6.1 Multivariate FPCA 91
4.6.2 MFPCA Displays 93
4.6.3 Multivariate Functional Principal Component Kriging 94
4.6.4 Mixing Temperature and Precipitation Curves 96
4.7 Discussion 98
4.A Appendices 100
4.A.1 Computation of the Kriging Variance 100
References 102
5 Geostatistical Analysis in Bayes Spaces: Probability Densities and Compositional Data 104
Alessandra Menafoglio, Piercesare Secchi, and Alberto Guadagnini
5.1 Introduction and Motivations 104
5.2 Bayes Hilbert Spaces: Natural Spaces for Functional Compositions 105
5.3 A Motivating Case Study: Particle-Size Data in Heterogeneous Aquifers –Data Description 108
5.4 Kriging Stationary Functional Compositions 110
5.4.1 Model Description 110
5.4.2 Data Preprocessing 112
5.4.3 An Example of Application 113
5.4.4 Uncertainty Assessment 116
5.5 Analyzing Nonstationary Fields of FCs 119
5.6 Conclusions and Perspectives 123
References 124
6 Spatial Functional Data Analysis for Probability Density Functions: Compositional Functional Data vs. Distributional Data Approach 128
Elvira Romano, Antonio Irpino, and Jorge Mateu
6.1 FDA and SDA When Data Are Densities 130
6.1.1 Features of Density Functions as Compositional Functional Data 131
6.1.2 Features of Density Functions as Distributional Data 135
6.2 Measures of Spatial Association for Georeferenced Density Functions 138
6.2.1 Identification of Spatial Clusters by Spatial Association Measures for Density Functions 139
6.3 Real Data Analysis 141
6.3.1 The SDA Distributional Approach 143
6.3.2 The Compositional–Functional Approach 145
6.3.3 Discussion 147
6.4 Conclusion 149
Acknowledgments 151
References 151
Part II Statistical Techniques for Spatially Correlated Functional Data 155
7 Clustering Spatial Functional Data 157
Vincent Vandewalle, Cristian Preda, and Sophie Dabo-Niang
7.1 Introduction 157
7.2 Model-Based Clustering for Spatial Functional Data 158
7.2.1 The Expectation–Maximization (EM) Algorithm 160
7.2.1.1 E Step 161
7.2.1.2 M Step 161
7.2.2 Model Selection 161
7.3 Descendant Hierarchical Classification (HC) Based on Centrality Methods 162
7.3.1 Methodology 164
7.4 Application 165
7.4.1 Model-Based Clustering 167
7.4.2 Hierarchical Classification 169
7.5 Conclusion 171
References 172
8 Nonparametric Statistical Analysis of Spatially Distributed Functional Data 175
Sophie Dabo-Niang, Camille Ternynck, Baba Thiam, and Anne-Françoise Yao
8.1 Introduction 175
8.2 Large Sample Properties 178
8.2.1 Uniform Almost Complete Convergence 180
8.3 Prediction 181
8.4 Numerical Results 184
8.4.1 Bandwidth Selection Procedure 184
8.4.2 Simulation Study 185
8.5 Conclusion 193
8.A Appendix 194
8.A.1 Some Preliminary Results for the Proofs 194
8.A.2 Proofs 196
8.A.2.1 Proof of Theorem 8.1 196
8.A.2.2 Proof of Lemma A.3 196
8.A.2.3 Proof of Lemma A.4 196
8.A.2.4 Proof of Lemma A.5 201
8.A.2.5 Proof of Lemma A.6 201
8.A.2.6 Proof of Theorem 8.2 202
References 207
9 A Nonparametric Algorithm for Spatially Dependent Functional Data: Bagging Voronoi for Clustering, Dimensional Reduction, and Regression 211
Valeria Vitelli, Federica Passamonti, Simone Vantini, and Piercesare Secchi
9.1 Introduction 211
9.2 The Motivating Application 212
9.2.1 Data Preprocessing 214
9.3 The Bagging Voronoi Strategy 216
9.4 Bagging Voronoi Clustering (BVClu) 218
9.4.1 BVClu of the Telecom Data 221
9.4.1.1 Setting the BVClu Parameters 221
9.4.1.2 Results 223
9.5 Bagging Voronoi Dimensional Reduction (BVDim) 223
9.5.1 BVDim of the Telecom Data 225
9.5.1.1 Setting the BVDim Parameters 225
9.5.1.2 Results 227
9.6 Bagging Voronoi Regression (BVReg) 231
9.6.1 Covariate Information: The DUSAF Data 232
9.6.2 BVReg of the Telecom Data 234
9.6.2.1 Setting the BVReg Parameters 234
9.6.2.2 Results 235
9.7 Conclusions and Discussion 236
References 239
10 Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data 242
Alessia Pini and Simone Vantini
10.1 Introduction 242
10.2 Methodology 244
10.2.1 Comparing Means of Two Functional Populations 244
10.2.2 Extensions 248
10.2.2.1 Multiway FANOVA 249
10.3 Data Analysis 250
10.4 Conclusion and FutureWorks 256
References 258
11 Modeling Spatially Dependent Functional Data by Spatial Regression with Differential Regularization 260
Mara S. Bernardi and Laura M. Sangalli
11.1 Introduction 260
11.2 Spatial Regression with Differential Regularization for Geostatistical Functional Data 264
11.2.1 A Separable Spatiotemporal Basis System 265
11.2.2 Discretization of the Penalized Sum-of-Square Error Functional 268
11.2.3 Properties of the Estimators 271
11.2.4 Model Without Covariates 273
11.2.5 An Alternative Formulation of the Model 274
11.3 Simulation Studies 274
11.4 An Illustrative Example: Study of the Waste Production in Venice Province 278
11.4.1 The Venice Waste Dataset 278
11.4.2 Analysis of Venice Waste Data by Spatial Regression with Differential Regularization 279
11.5 Model Extensions 282
References 283
12 Quasi-maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models 286
Mohamed-Salem Ahmed, Laurence Broze, Sophie Dabo-Niang, and Zied Gharbi
12.1 Introduction 286
12.2 Model 288
12.2.1 Truncated Conditional Likelihood Method 291
12.3 Results and Assumptions 293
12.4 Numerical Experiments 298
12.4.1 Monte Carlo Simulations 298
12.4.2 Real Data Application 305
12.5 Conclusion 312
12.A Appendix 313
Proof of Proposition 12.A.1 313
Proof of Theorem 12.1 314
Proof of Theorem 12.2 317
Proof of Theorem 12.3 319
Proof of Lemma 12.A.2 322
Proof of Lemma 12.A.3 322
Proof of Lemma 12.A.5 323
References 325
13 Spatial Prediction and Optimal Sampling for Multivariate Functional Random Fields 329
Martha Bohorquez, Ramón Giraldo, and Jorge Mateu
13.1 Background 329
13.1.1 Multivariate Spatial Functional Random Fields 329
13.1.2 Functional Principal Components 330
13.1.3 The Spatial Random Field of Scores 331
13.2 Functional Kriging 332
13.2.1 Ordinary Functional Kriging (OFK) 332
13.2.2 Functional Kriging Using Scalar Simple Kriging of the Scores (FKSK) 333
13.2.3 Functional Kriging Using Scalar Simple Cokriging of the Scores (FKCK) 333
13.3 Functional Cokriging 336
13.3.1 Cokriging with Two Functional Random Fields 336
13.3.2 Cokriging with P Functional Random Fields 338
13.4 Optimal Sampling Designs for Spatial Prediction of Functional Data 340
13.4.1 Optimal Spatial Sampling for OFK 341
13.4.2 Optimal Spatial Sampling for FKSK 341
13.4.3 Optimal Spatial Sampling for FKCK 342
13.4.4 Optimal Spatial Sampling for Functional Cokriging 343
13.5 Real Data Analysis 344
13.6 Discussion and Conclusions 348
References 348
Part III Spatio–Temporal Functional Data 351
14 Spatio–temporal Functional Data Analysis 353
Gregory Bopp, John Ensley, Piotr Kokoszka, and Matthew Reimherr
14.1 Introduction 353
14.2 Randomness Test 355
14.3 Change-Point Test 359
14.4 Separability Tests 362
14.5 Trend Tests 365
14.6 Spatio–Temporal Extremes 369
References 373
15 A Comparison of Spatiotemporal and Functional Kriging Approaches 375
Johan Strandberg, Sara Sjöstedt de Luna, and Jorge Mateu
15.1 Introduction 375
15.2 Preliminaries 376
15.3 Kriging 378
15.3.1 Functional Kriging 378
15.3.1.1 Ordinary Kriging for Functional Data 378
15.3.1.2 Pointwise Functional Kriging 380
15.3.1.3 Functional Kriging Total Model 381
15.3.2 Spatiotemporal Kriging 382
15.3.3 Evaluation of Kriging Methods 384
15.4 A Simulation Study 385
15.4.1 Separable 385
15.4.2 Non-separable 390
15.4.3 Nonstationary 391
15.5 Application: Spatial Prediction of Temperature Curves in the Maritime Provinces of Canada 394
15.6 Concluding Remarks 400
References 400
16 From Spatiotemporal Smoothing to Functional Spatial Regression: a Penalized Approach 403
Maria Durban, Dae-Jin Lee, María del Carmen Aguilera Morillo, and Ana M. Aguilera
16.1 Introduction 403
16.2 Smoothing Spatial Data via Penalized Regression 404
16.3 Penalized Smooth Mixed Models 407
16.4 P-spline Smooth ANOVA Models for Spatial and Spatiotemporal data 409
16.4.1 Simulation Study 411
16.5 P-spline Functional Spatial Regression 413
16.6 Application to Air Pollution Data 415
16.6.1 Spatiotemporal Smoothing 416
16.6.2 Spatial Functional Regression 416
Acknowledgments 421
References 421
Index 424
"Spatial functional data (SFD) arises when we have functional data (curves or images) at each one of the several sites or areas of a region. Statistics for SFD is concerned with the application of methods for modeling this type of data. All the fields of spatial statistics (point patterns, areal data and geostatistics) have been adapted to the study of SFD. For example, in point patterns analysis, the functional mark correlation function is proposed as a counterpart of the mark correlation function; in areal data, analysis of a functional areal dataset consisting of population pyramids for 38 neighborhoods in Barcelona (Spain) has been proposed; and in geostatistical analysis diverse approaches for kriging of functional data have been given. In the last few years, some alternatives have been adapted for considering models for SFD, where the estimation of the spatial correlation is of interest. When a functional variable is measured in sites of a region, i.e. when there is a realisation of a functional random field (spatial functional stochastic process), it is important to test for significant spatial autocorrelation and study this correlation if present. Assessing whether SFD are or are not spatially correlated allows us to properly formulate a functional model. However, searching in the literature, it is clear that amongst the several categories of spatial functional methods, functional geostatistics has been much more developed considering both new methodological approaches and analysis of a wide range of case studies covering a wealth of varied fields of applications"-- Provided by publisher.
About the Authors
Jorge Mateu is a full professor of Statistics at the Department of Mathematics of University Jaume I of Castellon, where he has worked for the past 20 years. His main fields of interest are stochastic processes in their wide sense with a particular focus on spatial and spatio-temporal point processes and geostatistics. He has published more than 150 papers in peer-reviewed international journals, and he is co-author of several proceedings and research books. He has organised several international conferences with a focus on modelling space-time processes, and leads the organising committee of a series of biannual conferences (called METMA, eight by now) co-sponsorised by TIES, for which he was also Secretary. He currently sits on the editorial boards of Spatial Statistics, Journal of Environmental Statistics, Stochastic Environmental Research and Risk Assessment, Environmetrics, and Journal of Agricultural, Biological, and Environmental Statistics. Prof. Mateu is also director of the Unit "Statistical Modelling of Crime Data", based in the Department of Mathematics, University Jaume I of Castellon, and he is co-director of the Erasmus Mundus Master in Geospatial Technologies, funded by the European Commission.
Ramon Giraldo is currently a full professor of Statistics at the Department of Statistics at the Universidad Nacional de Colombia, where he has worked for more than 10 years. His main fields of interest are non-parametric statistics, functional data analysis and spatial and spatio-temporal geostatistics. He has published more than 20 papers in peer-reviewed international journals, and he has been supervisor of 2 Doctoral Thesis and more than 10 Master Thesis. He has been Academic Coordinator, Head of Department and Research Coordinator at the Statistics Department of Universidad Nacional de Colombia. He is currently Editor-in-Chief of Colombian Journal of Statistics.
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