Statistics for Spatio-Temporal Data (eBook)

Noel Cressie, Christopher K. Wikle (Autoren)

eBook Download: EPUB

2015 | 1. Auflage
624 Seiten
John Wiley & Sons (Verlag)
978-1-119-24306-9 (ISBN)

Infos

Winner of the 2013 DeGroot Prize.

A state-of-the-art presentation of spatio-temporal processes,
bridging classic ideas with modern hierarchical statistical
modeling concepts and the latest computational methods

Noel Cressie and Christopher K. Wikle, are also winners
of the 2011 PROSE Award in the Mathematics category, for the
book "Statistics for Spatio-Temporal Data" (2011),
published by John Wiley and Sons. (The PROSE awards, for
Professional and Scholarly Excellence, are given by the Association
of American Publishers, the national trade association of the US
book publishing industry.)

Statistics for Spatio-Temporal Data has now been
reprinted with small corrections to the text and
the bibliography. The overall content and pagination of the
new printing remains the same; the difference comes in
the form of corrections to typographical errors, editing of
incomplete and missing references, and some updated spatio-temporal
interpretations.

From understanding environmental processes and climate trends to
developing new technologies for mapping public-health data and the
spread of invasive-species, there is a high demand for statistical
analyses of data that take spatial, temporal, and spatio-temporal
information into account. Statistics for Spatio-Temporal
Data presents a systematic approach to key quantitative
techniques that incorporate the latest advances in statistical
computing as well as hierarchical, particularly Bayesian,
statistical modeling, with an emphasis on dynamical spatio-temporal
models.

Cressie and Wikle supply a unique presentation that
incorporates ideas from the areas of time series and spatial
statistics as well as stochastic processes. Beginning with separate
treatments of temporal data and spatial data, the book combines
these concepts to discuss spatio-temporal statistical methods for
understanding complex processes.

Topics of coverage include:

* Exploratory methods for spatio-temporal data, including
visualization, spectral analysis, empirical orthogonal function
analysis, and LISAs

* Spatio-temporal covariance functions, spatio-temporal kriging,
and time series of spatial processes

* Development of hierarchical dynamical spatio-temporal models
(DSTMs), with discussion of linear and nonlinear DSTMs and
computational algorithms for their implementation

* Quantifying and exploring spatio-temporal variability in
scientific applications, including case studies based on real-world
environmental data

Throughout the book, interesting applications demonstrate the
relevance of the presented concepts. Vivid, full-color graphics
emphasize the visual nature of the topic, and a related FTP site
contains supplementary material. Statistics for Spatio-Temporal
Data is an excellent book for a graduate-level course on
spatio-temporal statistics. It is also a valuable reference for
researchers and practitioners in the fields of applied mathematics,
engineering, and the environmental and health sciences.

Autor

Noel Cressie, PhD, 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, PhD, 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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 2.11.2015
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
• Themenwelt: Technik
• Schlagworte: biometrics • Biometrie • Environmental Science • Environmental Studies • Spezialthemen Statistik • Statistics • Statistics Special Topics • Statistik • Umweltforschung • Umweltwissenschaften
• ISBN-10: 1-119-24306-8 / 1119243068
• ISBN-13: 978-1-119-24306-9 / 9781119243069