A state-of-the-art presentation of spatio-temporal processes,
bridging classic ideas with modern hierarchical statistical
modeling concepts and the latest computational methods 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
Wiklesupply 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.
關於作者：
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.
目錄：
Preface.
Acknowledgments.
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 DataModelComputing 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: Linear Models.
7.3 Process Models for the DSTM: Nonlinear Models.
7.4 Process Models for the DSTM: Multivariate Models.
7.5 DSTM Parameter Models.
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: LinearGaussian Models.
8.3 Inference for the DSTM Parameters: LinearGaussian
Models.
8.4 Inference for the DSTM HM: NonlinearNon-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 Vector Winds.
Epilogue.
References.
Index.
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