Hierarchical Modeling and Analysis for Spatial Data

More than twice the size of its predecessor, this second edition reflects the major growth in spatial statistics as both a research area and an area of application. This edition includes four new chapters on spatial point patterns, big data, spatial and spatiotemporal gradient modeling, and the theoretical aspects of point-referenced modeling. It also expands several other chapters, updates the WinBUGS programs and R packages, doubles the number of exercises, and integrates many more color figures throughout the text.

List of contents

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.

About the author

Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand

Product details

• Authors: Sudipto Banerjee, Sudipto (University of Minnesota Banerjee, Banerjee Sudipto, Bradley P. Carlin, Carlin Bradley P., Alan E. Gelfand, Gelfand Alan E.
• Assisted by: Valerie Isham (Editor of the series), Niels Keiding (Editor of the series), Thomas A. Louis (Editor of the series), Howell Tong (Editor of the series)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 12.09.2014
• EAN: 9781439819173
• ISBN: 978-1-4398-1917-3
• No. of pages: 584
• Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; MATHEMATICS / Probability & Statistics / General, Probability & statistics, Probability and statistics