Statistical Methods for Spatio-Temporal Systems (CRC Monographs on Statistics & Applied Probability #107 (Hardcover))

Finkenstadt, Barbel; Held, Leonhard; Isham, Valerie

ISBN 10: 1584885939 / ISBN 13: 9781584885931

Editore: Chapman and Hall/CRC, 2006

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This is a new hardcover first edition, first printing copy, 286 pages with index, no DJ, dark green spine.

Codice articolo 103963

Dati bibliografici

• Titolo: Statistical Methods for Spatio-Temporal Systems (CRC Monographs on Statistics & Applied Probability #107 (Hardcover))
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 1584885939
• ISBN 13: 9781584885931
• Rilegatura: Hardcover
• Edizione: 1st Edition
• Condizione: New
• Condizione della sovraccoperta: No Jacket

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Bärbel Finkenstädt, Leonhard Held, Valerie Isham