Statistical Methods for Spatio-temporal Systems

Barbel Finkenstadt, Leonhard Held, Valerie Isham - Collection Monographs on Statistics and Applied Probability

300 pages, parution le 30/10/2006

Ajouter à une liste

Indisponible

Résumé

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.

L'auteur - Barbel Finkenstadt

Bärbel Finkenstadt University of Warwick, Coventry, UK

L'auteur - Leonhard Held

Leonhard Held:University of Munich, Munich, Germany

L'auteur - Valerie Isham

Valerie Isham University College, London, UK

Sommaire

Spatio-Temporal Point Processes: Methods and Applications
Spatio-Temporal Modeling-With a View to Biological Growth
Using Transforms to Analyze Space-Time Processes
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry
Space-Time Modeling of Rainfall for Continuous Simulation
A Primer on Space-Time Modeling from a Bayesian Perspective

Voir tout

Replier

Caractéristiques techniques

	PAPIER
Éditeur(s)	Chapman and Hall / CRC
Auteur(s)	Barbel Finkenstadt, Leonhard Held, Valerie Isham
Collection	Monographs on Statistics and Applied Probability
Parution	30/10/2006
Nb. de pages	300
Format	16,5 x 24,5
Couverture	Relié
Poids	570g
Intérieur	Noir et Blanc
EAN13	9781584885931
ISBN13	978-1-58488-593-1