Statistical Methods for Spatio-Temporal Systems

Barbel Finkenstadt, Leonhard Held, Valerie Isham

October 20, 2006 by Chapman and Hall/CRC
Reference - 286 Pages - 16 Color & 122 B/W Illustrations
ISBN 9781584885931 - CAT# C5939
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability


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  • 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
  • Summary

    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.


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