Statistical Methods for Spatio-Temporal Systems
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advanced spatio-temporal statistical modeling
air quality statistics
Bayesian inference
Category=PBT
Conditional Intensity
Conditional Intensity Function
covariance
Covariance Function
Cox Process
CRPS
De Iaco
ecological data analysis
Empirical Orthogonal Functions
epidemic modeling
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
full
Full Conditionals
Full Symmetry
function
functions
Gastroenteric Disease
hydrological risk assessment
Log Gaussian Cox Process
markov
Markov Point Processes
Maximum Pseudolikelihood
Monte Carlo Maximum Likelihood
MRF Model
Pairwise Interaction Point Processes
Periodogram Values
Po Ra
point
Point Process
poisson
Poisson Point Process
process
processes
Space Time Covariances
Spatial Temporal Covariance
Spatio Temporal Point Process
stationary
Stationary Covariance Functions
stochastic simulation
Temporal Nonstationarity
Product details
- ISBN 9781584885931
- Weight: 740g
- Dimensions: 156 x 234mm
- Publication Date: 20 Oct 2006
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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 model.
Bärbel Finkenstädt, Leonhard Held, Valerie Isham
