Statistics for spatio-temporal data / Noel Cressie, Christopher K. Wikle
Tipo de material:
TextoSeries Wiley series in probability and statisticsDetalles de publicación: Hoboken : John Wiley & Sons, 2011 Descripción: XXII, 588 p. : graf.. ; 24 cmISBN: 978-0-471-69274-4Tema(s): Análisis espacial (Estadística) | Procesos estocásticos | Análisis de series temporalesResumen: INDICE: List of Figures. List of Tables. Preface. 1. Space-Time: The Next Frontier. 2. Statistical Preliminaries. 2.1 Conditional Probabilities and Hierarchical Modeling (HM). 2.2 Inference and Diagnostics. 2.3 Computation of the Posterior Distribution. 2.4 Graphical Representations of Statistical Dependencies. 2.5 Data/Model/Computing Compromises. 3. Fundamentals of Temporal Processes. 3.1 Characterization of Temporal Processes. 3.2 Introduction to Deterministic Dynamical Systems. 3.3 Time Series Preliminaries. 3.4 Basic Time Series Models. 3.5 Spectral Representation of Temporal Processes. 3.6 Hierarchical Modeling of Time Series. 3.7 Bibliographic Notes. 4. Fundamentals of Spatial Random Processes. 4.1 Geostatistical processes. 4.2 Lattice Processes. 4.3 Spatial Point Processes. 4.4 Random Sets. 4.5 Bibliographic Notes. 5. Exploratory Methods for Spatio-Temporal Data. 5.1 Visualization. 5.2 Spectral Analysis. 5.3 Empirical Orthogonal Function (EOF) Analysis. 5.4 Extensions of EOF Analysis. 5.5 Principal Oscillation Patterns (POPs). 5.6 Spatio-Temporal Canonical Correlation Analysis (CCA). 5.7 Spatio-Temporal Field Comparisons. 5.8 Bibliographic Notes. 6. Spatio-Temporal Statistical Models. 6.1 Spatio-temporal covariance functions. 6.2 Spatio-temporal Kriging. 6.3 Stochastic Differential and Difference Equations. 6.4 Time Series of Spatial Processes. 6.5 Spatio-temporal point processes. 6.6 Spatio-Temporal Components-of-variations Models. 6.7 Bibliographic Notes. 7. Hierarchical Dynamical Spatio-Temporal Models. 7.1 Data Models for the DSTM. 7.2 Process Models for the DSTM: LinearModels. 7.3 Process Models for the DSTM: NonlinearModels. 7.4 Process Models for the DSTM: Multivariate Models. 7.5 DSTM ParameterModels. 7.6 Dynamical Design of Monitoring Networks. 7.7 Switching the Emphasis of Time and Space. 7.8 Bibliographic Notes. 8. Hierarchical DSTMs: Implementation and Inference. 8.1 DSTM Process: General Implementation and Inference. 8.2 Inference for the DSTM Process: Linear/Gaussian Models. 8.3 Inference for the DSTM Parameters: Linear/Gaussian Models. 8.4 Inference for the DSTM HM: Nonlinear/Non-Gaussian Models. 8.5 Bibliographic Notes. 9. Hierarchical DSTMs: Examples. 9.1 Long-Lead Forecasting of Tropical Pacific Sea Surface Temperatures. 9.2 Remotely Sensed Aerosol Optical Depth. 9.3 Modeling and Forecasting the Eurasian Collared Dove Invasion. 9.4 Mediterranean Surface VectorWinds. 10. Epilogue. References.
| Tipo de ítem | Biblioteca de origen | Signatura | URL | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
|---|---|---|---|---|---|---|---|
| Monografías | 02. BIBLIOTECA CAMPUS PUERTO REAL | 519.2/CRE/sta (Navegar estantería(Abre debajo)) | Texto completo | Prestado | 31/01/2017 | 3743474384 |
Índice
Bibliografía: p. 523-569
INDICE: List of Figures. List of Tables. Preface. 1. Space-Time: The Next Frontier. 2. Statistical Preliminaries. 2.1 Conditional Probabilities and Hierarchical Modeling (HM). 2.2 Inference and Diagnostics. 2.3 Computation of the Posterior Distribution. 2.4 Graphical Representations of Statistical Dependencies. 2.5 Data/Model/Computing Compromises. 3. Fundamentals of Temporal Processes. 3.1 Characterization of Temporal Processes. 3.2 Introduction to Deterministic Dynamical Systems. 3.3 Time Series Preliminaries. 3.4 Basic Time Series Models. 3.5 Spectral Representation of Temporal Processes. 3.6 Hierarchical Modeling of Time Series. 3.7 Bibliographic Notes. 4. Fundamentals of Spatial Random Processes. 4.1 Geostatistical processes. 4.2 Lattice Processes. 4.3 Spatial Point Processes. 4.4 Random Sets. 4.5 Bibliographic Notes. 5. Exploratory Methods for Spatio-Temporal Data. 5.1 Visualization. 5.2 Spectral Analysis. 5.3 Empirical Orthogonal Function (EOF) Analysis. 5.4 Extensions of EOF Analysis. 5.5 Principal Oscillation Patterns (POPs). 5.6 Spatio-Temporal Canonical Correlation Analysis (CCA). 5.7 Spatio-Temporal Field Comparisons. 5.8 Bibliographic Notes. 6. Spatio-Temporal Statistical Models. 6.1 Spatio-temporal covariance functions. 6.2 Spatio-temporal Kriging. 6.3 Stochastic Differential and Difference Equations. 6.4 Time Series of Spatial Processes. 6.5 Spatio-temporal point processes. 6.6 Spatio-Temporal Components-of-variations Models. 6.7 Bibliographic Notes. 7. Hierarchical Dynamical Spatio-Temporal Models. 7.1 Data Models for the DSTM. 7.2 Process Models for the DSTM: LinearModels. 7.3 Process Models for the DSTM: NonlinearModels. 7.4 Process Models for the DSTM: Multivariate Models. 7.5 DSTM ParameterModels. 7.6 Dynamical Design of Monitoring Networks. 7.7 Switching the Emphasis of Time and Space. 7.8 Bibliographic Notes. 8. Hierarchical DSTMs: Implementation and Inference. 8.1 DSTM Process: General Implementation and Inference. 8.2 Inference for the DSTM Process: Linear/Gaussian Models. 8.3 Inference for the DSTM Parameters: Linear/Gaussian Models. 8.4 Inference for the DSTM HM: Nonlinear/Non-Gaussian Models. 8.5 Bibliographic Notes. 9. Hierarchical DSTMs: Examples. 9.1 Long-Lead Forecasting of Tropical Pacific Sea Surface Temperatures. 9.2 Remotely Sensed Aerosol Optical Depth. 9.3 Modeling and Forecasting the Eurasian Collared Dove Invasion. 9.4 Mediterranean Surface VectorWinds. 10. Epilogue. References.
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