Presents a variety of statistical methods, including likelihood-based, nonparametric smoothing, spectral, Fourier, wavelet, and Markov chain Monte Carlo

Describes recent advances in space-time covariance functions and stochastic growth models based on spatio-temporal point processes and Lévy bases

Covers key topics, such as point processes, dynamics, modeling, data analysis, Bayesian methods, and geostatistics

Illustrates methods with color images as well as real-world examples, case studies, and applications from epidemiology, geology, and climatology

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.

Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo, illustrating the methods with a variety of data examples, such as temperature surfaces, dioxin concentrations, ozone concentrations, and a well-established deterministic dynamical weather model.

Preface

Spatio-Temporal Point Processes: Methods and Applications
Peter J. Diggle

Spatio-Temporal Modeling-With a View to Biological Growth
Eva B. Vedel Jensen, Kristjana Ýr Jónsdóttir, Jürgen Schmiegel, and Ole E. Barndorff-Nielsen

Using Transforms to Analyze Space-Time Processes
Montserrat Fuentes, Peter Guttorp, and Paul D. Sampson

Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry
Tilmann Gneiting, Marc G. Genton, and Peter Guttorp

Space-Time Modeling of Rainfall for Continuous Simulation
Richard E. Chandler, Valerie Isham, Enrica Bellone, Chi Yang, and Paul Northrop

A Primer on Space-Time Modeling from a Bayesian Perspective
Dave Higdon

Index

Editorial Reviews

"… an extremely well-written summary of important topics in the analysis of spatial point processes. … The authors do an excellent job focusing on those theoretical concepts and methods that are most important in applied research. … this is the first text to tackle difficult issues of simulation-based inference … The authors have a very impressive knack for explaining complicated topics very clearly … Its excellent survey of the vast array of models is reason enough to own it. As computer technology and speed advance … the authors' clear, detailed, and comprehensive survey of simulation methods for spatial point processes will become increasingly important."
-Journal of the American Statistical Association
xtremely well-written summary of important topics in the analysis of spatial point processes. … The authors do an excellent job focusing on those theoretical concepts and methods that are most important in applied research. … this is the first text to tackle difficult issues of simulation-based inference … The authors have a very impressive knack for explaining complicated topics very clearly … Its excellent survey of the vast array of models is reason enough to own it. As computer technology and speed advance … the authors' clear, detailed, and comprehensive survey of simulation methods for spatial point processes will become increasingly important."
-Journal of the American Statistical Association