Statistics for spatio-temporal data / Noel Cressie, Christopher K. Wikle
Type de document : MonographieCollection : Wiley series in probability and statisticsLangue : anglais.Pays: Etats Unis.Éditeur : Hoboken (N.J.) : John Wiley, cop. 2011Description : 1 vol. (XXII-588 p.) : ill. ; 24 cmISBN: 9780471692744.ISSN: 1940-6517.Bibliographie : Bibliogr. p. 523-569. Index.Sujet MSC : 62-02, Research exposition (monographs, survey articles) pertaining to statistics62M30, Statistics - Inference from stochastic processes, Inference from spatial processes
62M10, Statistics - Inference from stochastic processes, Time series, auto-correlation, regression, etc.
62M15, Statistics - Inference from stochastic processes, Inference from stochastic processes and spectral analysisEn-ligne : MathSciNet | zbMath | site de l'éditeur
| Item type | Current library | Call number | Status | Date due | Barcode |
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Monographie
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CMI Salle 1 | 62 CRE (Browse shelf(Opens below)) | Available | 10909-01 |
Publisher’s description: A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods.
From understanding environmental processes and climate trends to developing new technologies for mapping and public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. The book presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models.
The authors supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes.
Topics of coverage include:
– Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAs;
– Spatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processes;
– Development of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementation;
– Quantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data.
Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, fullcolor graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. This is an excellent book for a graduate level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Bibliogr. p. 523-569. Index
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