top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Multiple-point geostatistics : stochastic modeling with training images / / Gregoire Mariethoz and Jef Caers
Multiple-point geostatistics : stochastic modeling with training images / / Gregoire Mariethoz and Jef Caers
Autore Mariethoz Gregoire
Pubbl/distr/stampa Chichester, England ; ; Oxford, England ; ; Hoboken, New Jersey : , : Wiley Blackwell, , 2015
Descrizione fisica 1 online resource (379 p.)
Disciplina 551.01/5195
Soggetto topico Geology - Statistical methods
Geological modeling
ISBN 1-118-66293-8
1-118-66295-4
1-118-66294-6
Classificazione SCI031000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Multiple-point geostatistics; Contents; Preface; Acknowledgments; Part I Concepts; 1 Hiking in the Sierra Nevada; 1.1 An imaginary outdoor adventure company: Buena Sierra; 1.2 What lies ahead; 2 Spatial estimation based on random function theory; 2.1 Assumptions of stationarity; 2.2 Assumption of stationarity in spatial problems; 2.3 The kriging solution; 2.3.1 Unbiasedness condition; 2.3.2 Minimizing squared loss; 2.4 Estimating covariances; 2.5 Semivariogram modeling; 2.6 Using a limited neighborhood; 2.7 Universal kriging; 2.8 Semivariogram modeling for universal kriging
2.9 Simple trend example case2.10 Nonstationary covariances; 2.11 Assessment; References; 3 Universal kriging with training images; 3.1 Choosing for random function theory or not?; 3.2 Formulation of universal kriging with training images; 3.2.1 Zero error-sum condition; 3.2.2 Minimum sum of square error condition; 3.3 Positive definiteness of the sop matrix; 3.4 Simple kriging with training images; 3.5 Creating a map of estimates; 3.6 Effect of the size of the training image; 3.7 Effect of the nature of the training image; 3.8 Training images for nonstationary modeling
3.9 Spatial estimation with nonstationary training images3.10 Summary of methodological differences; References; 4 Stochastic simulations based on random function theory; 4.1 The goal of stochastic simulations; 4.2 Stochastic simulation: Gaussian theory; 4.3 The sequential Gaussian simulation algorithm; 4.4 Properties of multi-Gaussian realizations; 4.5 Beyond Gaussian or beyond covariance?; References; 5 Stochastic simulation without random function theory; 5.1 Direct sampling; 5.1.1 Relying on information theory; 5.1.2 Application of direct sampling to Walker Lake
5.2 The extended normal equation5.2.1 Formulation; 5.2.2 The RAM solution; 5.2.3 Single normal equations simulation for Walker Lake; 5.2.4 The problem of conditioning; 5.3 Simulation by texture synthesis; 5.3.1 Computer graphics; 5.3.2 Image quilting; References; 6 Returning to the Sierra Nevada; Reference; Part II Methods; 1 Introduction; 2 The algorithmic building blocks; 2.1 Grid and pointset representations; 2.2 Multivariate grids; 2.3 Neighborhoods; 2.4 Storage and restitution of data events; 2.4.1 Raw storage of training image; 2.4.2 Cross-correlation based convolution
2.4.3 Partial convolution2.4.4 Tree storage; 2.4.5 List storage; 2.4.6 Clustering of patterns; 2.4.7 Parametric representation of patterns; 2.5 Computing distances; 2.5.1 Norms; 2.5.2 Hausdorff distance; 2.5.3 Invariant distances; 2.5.4 Change of variable; 2.5.5 Distances between distributions; 2.6 Sequential simulation; 2.6.1 Random path; 2.6.2 Unilateral path; 2.6.3 Patch-based methods; 2.6.4 Patch carving; 2.7 Multiple grids; 2.8 Conditioning; 2.8.1 The different types of data; 2.8.2 Different types of data: an example; 2.8.3 Steering proportions; References
3 Multiple-point geostatistics algorithms
Record Nr. UNINA-9910812133303321
Mariethoz Gregoire  
Chichester, England ; ; Oxford, England ; ; Hoboken, New Jersey : , : Wiley Blackwell, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multiple-point geostatistics : stochastic modeling with training images / Gregoire Mariethoz and Jef Caers
Multiple-point geostatistics : stochastic modeling with training images / Gregoire Mariethoz and Jef Caers
Autore Mariethoz, Gregoire
Pubbl/distr/stampa Chichester, West Sussex ; Hoboken, NJ : Wiley., 2015
Descrizione fisica p. ; cm
Disciplina 551.015195
Altri autori (Persone) Caers, Jefauthor
Soggetto topico Geology - Statistical methods
Geological modeling
ISBN 9781118662755 (hardback)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991002901669707536
Mariethoz, Gregoire  
Chichester, West Sussex ; Hoboken, NJ : Wiley., 2015
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Petroleum geostatistics [[electronic resource] /] / Jef Caers
Petroleum geostatistics [[electronic resource] /] / Jef Caers
Autore Caers Jef
Pubbl/distr/stampa Richardson, Tex., : Society of Petroleum Engineers, 2005
Descrizione fisica 1 online resource (vii, 88 p.) : ill
Disciplina 665.5015195
Collana SPE interdisciplinary primer series
Soggetto topico Geology - Statistical methods
Petroleum - Geology
Geological modeling
Hydrocarbon reservoirs
Soggetto genere / forma Electronic books.
ISBN 9781613992289 (e-book)
9781555631062 (pbk.)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. The Role of Geostatistics in Reservoir Modeling -- 2. Modeling Geological Continuity -- 3. Building High-Resolution Geocellular Models -- 4. Geostatistical Methods for History Matching Under Geological Control -- 5. Modeling Uncertainty -- Index.
Record Nr. UNINA-9910462006603321
Caers Jef  
Richardson, Tex., : Society of Petroleum Engineers, 2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A practical primer on geostatistics [[electronic resource] /] / by Ricardo A. Olea
A practical primer on geostatistics [[electronic resource] /] / by Ricardo A. Olea
Autore Olea R. A (Ricardo A.)
Edizione [Version 1.4, December 2018.]
Pubbl/distr/stampa Reston, Va. : , : U.S. Geological Survey, , 2009
Descrizione fisica ii, 346 pages : digital, PDF file
Collana Open-file report
Soggetto topico Geology - Statistical methods
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910698926103321
Olea R. A (Ricardo A.)  
Reston, Va. : , : U.S. Geological Survey, , 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantifying uncertainty in subsurface systems / / Céline Scheidt, Lewis Li, Jef Caers
Quantifying uncertainty in subsurface systems / / Céline Scheidt, Lewis Li, Jef Caers
Autore Scheidt Celine
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , 2018
Descrizione fisica 1 online resource (294 pages)
Disciplina 550.72
Collana Geophysical monograph
Soggetto topico Geology - Statistical methods
Geology - Mathematical models
Earth sciences - Statistical methods
ISBN 1-119-32586-2
1-119-32587-0
1-119-32588-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555060503321
Scheidt Celine  
Hoboken, NJ : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantifying uncertainty in subsurface systems / / Céline Scheidt, Lewis Li, Jef Caers
Quantifying uncertainty in subsurface systems / / Céline Scheidt, Lewis Li, Jef Caers
Autore Scheidt Celine
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , 2018
Descrizione fisica 1 online resource (294 pages)
Disciplina 550.72
Collana Geophysical monograph
Soggetto topico Geology - Statistical methods
Geology - Mathematical models
Earth sciences - Statistical methods
ISBN 1-119-32586-2
1-119-32587-0
1-119-32588-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910815447503321
Scheidt Celine  
Hoboken, NJ : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Seismic reservoir modeling : theory, examples, and algorithms / / Dario Grana, Tapan Mukerji, Philippe Doyen
Seismic reservoir modeling : theory, examples, and algorithms / / Dario Grana, Tapan Mukerji, Philippe Doyen
Autore Grana Dario
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley Blackwell, , [2021]
Descrizione fisica 1 online resource (259 pages)
Disciplina 551.220151
Soggetto topico Seismology - Mathematics
Geology - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-119-08620-5
1-119-08619-1
1-119-08621-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555008403321
Grana Dario  
Hoboken, New Jersey : , : Wiley Blackwell, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Seismic reservoir modeling : theory, examples, and algorithms / / Dario Grana, Tapan Mukerji, Philippe Doyen
Seismic reservoir modeling : theory, examples, and algorithms / / Dario Grana, Tapan Mukerji, Philippe Doyen
Autore Grana Dario
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley Blackwell, , [2021]
Descrizione fisica 1 online resource (259 pages)
Disciplina 551.220151
Soggetto topico Seismology - Mathematics
Geology - Statistical methods
ISBN 1-119-08620-5
1-119-08619-1
1-119-08621-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830987503321
Grana Dario  
Hoboken, New Jersey : , : Wiley Blackwell, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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  
Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Montero José María  
Chichester, West Sussex, UK : , : John Wiley and Sons, Inc., , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui