The first statistics book devoted to spatio-temporal models, Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling that will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. The book describes recent advances and presents a variety of statistical methods, including likelihood-based, nonparametric smoothing, spectral, Fourier, wavelet, and Markov chain Monte Carlo. The methods are illustrated with color images as well as real-world examples, case studies, and applications from epidemiology, geology, and climatology. Key topics include point processes, dynamics, modeling, data analysis, Bayesian methods, and geostatistics.
PrefaceSpatio-Temporal Point Processes: Methods and ApplicationsPeter J. DiggleSpatio-Temporal Modeling-With a View to Biological GrowthEva B. Vedel Jensen, Kristjana r J nsd ttir, Jnrgen Schmiegel, and Ole E. Barndorff-NielsenUsing Transforms to Analyze Space-Time ProcessesMontserrat Fuentes, Peter Guttorp, and Paul D. SampsonGeostatistical Space-Time Models, Stationarity, Separability, and Full SymmetryTilmann Gneiting, Marc G. Genton, and Peter GuttorpSpace-Time Modeling of Rainfall for Continuous SimulationRichard E. Chandler, Valerie Isham, Enrica Bellone, Chi Yang, and Paul NorthropA Primer on Space-Time Modeling from a Bayesian PerspectiveDave HigdonIndex