Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems

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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 more

Product details

  • Hardback | 286 pages
  • 152.4 x 226.06 x 20.32mm | 498.95g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 122 black & white illustrations, 16 colour illustrations, 16 black & white tables
  • 1584885939
  • 9781584885931
  • 2,430,778

Table of contents

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 Yr Jonsdottir, Jurgen 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 Indexshow more