Description
Hierarchical Modeling and Analysis for Spatial Data (2nd Ed.)
Chapman & Hall/CRC Monographs on Statistics and Applied Probability Series
Authors: Banerjee Sudipto, Carlin Bradley P., Gelfand Alan E.
Language: English
Subjects for Hierarchical Modeling and Analysis for Spatial Data:
Keywords
Full Conditional; Full Conditional Distributions; Space and Space-Time Data Analysis and Modeling; MCMC Method; spatial statistics; Posterior Predictive Samples; spatial point patterns; Gaussian Process; big data and the predictive process; Data Set; spatial and spatiotemporal gradient modeling; Covariance Function; point-referenced modeling; Posterior Predictive Distribution; WinBUGS and R for exploratory data analysis and hierarchical modeling; MCMC Convergence; methods for multivariate and spatiotemporal modeling; Valid Covariance Function; data fusion/assimilation; Empirical Semivariogram; theoretical aspects of geostatistical modeling; Log Relative Risk; spatial boundary analysis and wombling; Posterior Median; MCMC Iteration; Geometric Anisotropy; Posterior Distribution; MCMC Chain; Point Pattern; Frailty Model; Bayesian Melding; Cox Process; Smoothing Parameters; Car Model; Predictive Process Model; Cross-covariance Function
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the book of Banerjee Sudipto, Carlin Bradley P., Gelfand Alan E.
562 p. · 17.8x25.4 cm · Hardback · Two-tone printing
Description
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Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling
Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application.
New to the Second Edition
- New chapter on spatial point patterns developed primarily from a modeling perspective
- New chapter on big data that shows how the predictive process handles reasonably large datasets
- New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling
- New chapter on the theoretical aspects of geostatistical (point-referenced) modeling
- Greatly expanded chapters on methods for multivariate and spatiotemporal modeling
- New special topics sections on data fusion/assimilation and spatial analysis for data on extremes
- Double the number of exercises
- Many more color figures integrated throughout the text
- Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages
The Only Comprehensive Treatment of the Theory, Methods, and Software
This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modelingfor spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.
Overview of Spatial Data Problems. Basics of Point-Referenced Data Models. Basics of Areal Data Models. Basics of Bayesian Inference. Spatial Misalignment. Hierarchical Models for Point-Process Data. Hierarchical Modeling for Univariate Spatial Data. Modeling Large Spatial and Spatial-Temporal Data Sets. Multivariate Spatial Modeling. Special Topics. Appendices. References. Author Index. Subject Index.
