Noel Cressie & Christopher K. Wikle 
Statistics for Spatio-Temporal Data [PDF ebook] 

Preface xv
Acknowledgments xix
1 Space-Time: The Next Frontier 1
2 Statistical Preliminaries 17
2.1 Conditional Probabilities and Hierarchical Modeling (HM),
20
2.2 Inference and Diagnostics, 33
2.3 Computation of the Posterior Distribution, 42
2.4 Graphical Representations of Statistical Dependencies,
48
2.5 Data/Model/Computing Compromises, 53
3 Fundamentals of Temporal Processes 55
3.1 Characterization of Temporal Processes, 56
3.2 Introduction to Deterministic Dynamical Systems, 59
3.3 Time Series Preliminaries, 80
3.4 Basic Time Series Models, 84
3.5 Spectral Representation of Temporal Processes, 100
3.6 Hierarchical Modeling of Time Series, 112
3.7 Bibliographic Notes, 116
4 Fundamentals of Spatial Random Processes 119
4.1 Geostatistical Processes, 124
4.2 Lattice Processes, 167
4.3 Spatial Point Processes, 204
4.4 Random Sets, 224
4.5 Bibliographic Notes, 231
5 Exploratory Methods for Spatio-Temporal Data 243
5.1 Visualization, 244
5.2 Spectral Analysis, 259
5.3 Empirical Orthogonal Function (EOF) Analysis, 266
5.4 Extensions of EOF Analysis, 271
5.5 Principal Oscillation Patterns (POPs), 279
5.6 Spatio-Temporal Canonical Correlation Analysis (CCA),
284
5.7 Spatio-Temporal Field Comparisons, 291
5.8 Bibliographic Notes, 292
6 Spatio-Temporal Statistical Models 297
6.1 Spatio-Temporal Covariance Functions, 304
6.2 Spatio-Temporal Kriging, 321
6.3 Stochastic Differential and Difference Equations, 327
6.4 Time Series of Spatial Processes, 336
6.5 Spatio-Temporal Point Processes, 347
6.6 Spatio-Temporal Components-of-Variation Models, 351
6.7 Bibliographic Notes, 356
7 Hierarchical Dynamical Spatio-Temporal Models 361
7.1 Data Models for the DSTM, 363
7.2 Process Models for the DSTM: Linear Models, 382
7.3 Process Models for the DSTM: Nonlinear Models, 403
7.4 Process Models for the DSTM: Multivariate Models, 418
7.5 DSTM Parameter Models, 425
7.6 Dynamical Design of Monitoring Networks, 430
7.7 Switching the Emphasis of Time and Space, 432
7.8 Bibliographic Notes, 433
8 Hierarchical DSTMs: Implementation and Inference
441
8.1 DSTM Process: General Implementation and Inference, 441
8.2 Inference for the DSTM Process: Linear/Gaussian Models,
444
8.3 Inference for the DSTM Parameters: Linear/Gaussian Models,
450
8.4 Inference for the Hierarchical DSTM: Nonlinear/Non-Gaussian
Models, 460
8.5 Bibliographic Notes, 472
9 Hierarchical DSTMs: Examples 475
9.1 Long-Lead Forecasting of Tropical Pacific Sea Surface
Temperatures, 476
9.2 Remotely Sensed Aerosol Optical Depth, 488
9.3 Modeling and Forecasting the Eurasian Collared Dove
Invasion, 499
9.4 Mediterranean Surface Vector Winds, 507
Epilogue 519
References 523
Index 571

Noel Cressie, Ph D, is Professor of Statistics and Director
of the Program in Spatial Statistics and Environmental Statistics
at The Ohio State University. A Fellow of the American Statistical
Association and the Institute of Mathematical Statistics, he has
published extensively in the areas of statistical modeling,
analysis of spatial and spatio-temporal data, and
empirical-Bayesian and Bayesian methods. He is a recipient of the
R.A. Fisher Lectureship, awarded by COPSS to recognize the
importance of statistical methods for scientific investigations.
Dr. Cressie is an advisor for the Wiley Series in Probability and
Statistics and the author of Statistics for Spatial Data,
Revised Edition.
Chirstopher K. Wikle, Ph D, is Professor of Statistics at
the University of Missouri. Dr. Wikle is a Fellow of the American
Statistical Association and the author of more than 100 articles on
the topics of spatio-temporal methodology, spatial statistics,
hierarchical models, Bayesian methods, and computational methods
for large data sets. His work is motivated by problems in
climatology, ecology, fisheries and wildlife, meteorology, and
oceanography.