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Biblioteca

MARC Lista (tabellare)
Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (xiii, 313 p.) : ill
Disciplina 550.1519542
Altri autori (Persone) KanevskiMikhail
Collana ISTE
Soggetto topico Geology - Statistical methods
Machine learning
Bayesian statistical decision theory
Soggetto genere / forma Electronic books.
ISBN 9780470611463 (e-book)
9781848210608 (hbk.)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Advanced Mapping of Environmental Data: Introduction -- Chapter 2. Environmental Monitoring Network Characterization and Clustering -- Chapter 3. Geostatistics: Spatial Predictions and Simulations -- Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms -- Chapter 5. Advanced Mapping of Environmental Spatial Data: Case Studies -- Chapter 6. Bayesian Maximum Entropy – BME -- Index.
Record Nr. UNINA-9910139505003321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (xiii, 313 p.) : ill
Disciplina 550.1519542
Altri autori (Persone) KanevskiMikhail
Collana ISTE
Soggetto topico Geology - Statistical methods
Machine learning
Bayesian statistical decision theory
ISBN 9780470611463 (e-book)
9781848210608 (hbk.)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Advanced Mapping of Environmental Data: Introduction -- Chapter 2. Environmental Monitoring Network Characterization and Clustering -- Chapter 3. Geostatistics: Spatial Predictions and Simulations -- Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms -- Chapter 5. Advanced Mapping of Environmental Spatial Data: Case Studies -- Chapter 6. Bayesian Maximum Entropy – BME -- Index.
Record Nr. UNINA-9910830444603321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Advanced mapping of environmental data [[electronic resource] ] : geostatistics, machine learning, and Bayesian maximum entropy / / edited by Mikhail Kanevski
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (xiii, 313 p.) : ill
Disciplina 550.1519542
Altri autori (Persone) KanevskiMikhail
Collana ISTE
Soggetto topico Geology - Statistical methods
Machine learning
Bayesian statistical decision theory
ISBN 9780470611463 (e-book)
9781848210608 (hbk.)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Advanced Mapping of Environmental Data: Introduction -- Chapter 2. Environmental Monitoring Network Characterization and Clustering -- Chapter 3. Geostatistics: Spatial Predictions and Simulations -- Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms -- Chapter 5. Advanced Mapping of Environmental Spatial Data: Case Studies -- Chapter 6. Bayesian Maximum Entropy – BME -- Index.
Record Nr. UNINA-9910840854903321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electronic geosciences
Electronic geosciences
Pubbl/distr/stampa Berlin ; ; Heidelberg, : Springer-Verlag, 1998-©2002
Soggetto topico Geology - Computer programs
Geology - Statistical methods
Soggetto genere / forma Periodicals.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNISA-996211936903316
Berlin ; ; Heidelberg, : Springer-Verlag, 1998-©2002
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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The Encyclopedia of Applied Geology / / editor, Charles W. Finkl
The Encyclopedia of Applied Geology / / editor, Charles W. Finkl
Edizione [First edition 1984.]
Pubbl/distr/stampa New York : , : Springer US : , : Imprint : Springer, , 1984
Descrizione fisica 1 online resource (637 pages)
Disciplina 624.1/51/0321
Collana Encyclopedia of Earth Sciences Series
Soggetto topico Structural geology
Mineralogy
Sedimentology
Geology - Statistical methods
Economic geology
Physical geography
Structural Geology
Quantitative Geology
Economic Geology
Physical Geography
ISBN 0-387-30842-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910483023603321
New York : , : Springer US : , : Imprint : Springer, , 1984
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Evaluating coinciding anomalies along a fault trace or other traverse--simulations and statistical procedures / / by Russell L. Wheeler and Katherine B. Krystinik
Evaluating coinciding anomalies along a fault trace or other traverse--simulations and statistical procedures / / by Russell L. Wheeler and Katherine B. Krystinik
Autore Wheeler Russell L.
Pubbl/distr/stampa [Reston, Va.] : , : Department of the Interior, U.S. Geological Survey, , 1988
Descrizione fisica 1 online resource (iii, 12 pages) : illustrations
Collana U.S. Geological Survey bulletin
Soggetto topico Geology - Statistical methods
Spatial analysis (Statistics)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Evaluation of coinciding anomalies, simulations and procedures
Record Nr. UNINA-9910707047703321
Wheeler Russell L.  
[Reston, Va.] : , : Department of the Interior, U.S. Geological Survey, , 1988
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Autore Pawlowsky-Glahn Vera
Pubbl/distr/stampa New York, : Oxford University Press, 2004
Descrizione fisica 1 online resource (204 p.)
Disciplina 551/.072
Altri autori (Persone) OleaR. A (Ricardo A.)
Collana Studies in mathematical geology
Soggetto topico Geology - Statistical methods
Multivariate analysis
Kriging
Soggetto genere / forma Electronic books.
ISBN 0-19-756551-4
1-280-84376-4
0-19-803831-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 2 Regionalized compositions; 3 Spatial covariance structure; 4 Concepts of null correlation; 5 Cokriging; 6 Practical aspects of compositional data analysis; 7 Application to real data; Summary and prospects; References; Index
Record Nr. UNINA-9910453667803321
Pawlowsky-Glahn Vera  
New York, : Oxford University Press, 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Autore Pawlowsky-Glahn Vera
Pubbl/distr/stampa New York, : Oxford University Press, 2004
Descrizione fisica 1 online resource (204 p.)
Disciplina 551/.072
Altri autori (Persone) OleaR. A (Ricardo A.)
Collana Studies in mathematical geology
Soggetto topico Geology - Statistical methods
Multivariate analysis
Kriging
ISBN 0-19-756551-4
1-280-84376-4
0-19-803831-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 2 Regionalized compositions; 3 Spatial covariance structure; 4 Concepts of null correlation; 5 Cokriging; 6 Practical aspects of compositional data analysis; 7 Application to real data; Summary and prospects; References; Index
Record Nr. UNINA-9910782236403321
Pawlowsky-Glahn Vera  
New York, : Oxford University Press, 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Geostatistical analysis of compositional data [[electronic resource] /] / Vera Pawlowsky-Glahn, Ricardo A. Olea
Autore Pawlowsky-Glahn Vera
Pubbl/distr/stampa New York, : Oxford University Press, 2004
Descrizione fisica 1 online resource (204 p.)
Disciplina 551/.072
Altri autori (Persone) OleaR. A (Ricardo A.)
Collana Studies in mathematical geology
Soggetto topico Geology - Statistical methods
Multivariate analysis
Kriging
ISBN 0-19-756551-4
1-280-84376-4
0-19-803831-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 2 Regionalized compositions; 3 Spatial covariance structure; 4 Concepts of null correlation; 5 Cokriging; 6 Practical aspects of compositional data analysis; 7 Application to real data; Summary and prospects; References; Index
Record Nr. UNINA-9910822417703321
Pawlowsky-Glahn Vera  
New York, : Oxford University Press, 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
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