Hierarchical Modeling and Analysis for Spatial Data
Banerjee, Sudipto; Gelfand, Alan E.; Carlin, Bradley P.
Language: English
Published by Chapman and Hall/CRC, 2025
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- Title
- Hierarchical Modeling and Analysis for Spatial Data
- Author
- Banerjee, Sudipto; Gelfand, Alan E.; Carlin, Bradley P.
- Publisher
- Chapman and Hall/CRC
- Publication year
- 2025
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1032508558
- ISBN 13
- 9781032508559
- Edition
- 3rd Edition
Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.
Key features of the third edition:
- A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasets
- Two new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectives
- A new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanisms
- An accessible introduction to GPS mapping, geodesic distances, and mathematical cartography
- An expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional data
- A thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniques
- A dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developments
With refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.
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About the Author
Alan E. Gelfand is The James B Duke Professor Emeritus of Statistical Science at Duke University. He also enjoys a secondary appointment as Professor of Environmental Science and Policy in the Nicholas School. Author of more than 330 papers and 6 books, Gelfand is internationally known for his contributions to applied statistics, Bayesian computation and Bayesian inference. For the past thirty years, Gelfand’s primary research focus has been in the area of statistical modeling for spatial and space-time data. He has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data. His chief areas of application include spatio-temporal environmental and ecological processes.
Sudipto Banerjee is Professor of Biostatistics and Senior Associate Dean for Academic Programs in the Fielding School of Public Health at the University of California, Los Angeles (UCLA). He holds joint appointments as a Professor in the UCLA Department of Statistics and Data Science and as an Affiliate faculty in the UCLA Institute of Environment and Sustainability. Banerjee has authored over 200 research articles, 2 textbooks, 2 committee reports for the National Research Council of the National Academies, and an edited handbook on spatial epidemiology. Banerjee is well-known for his research expertise and methodological advancements in Bayesian hierarchical modeling and inference for spatial-temporal data; theoretical and computational developments for Gaussian processes; environmental processes and their impact on public health; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis.
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