Hierarchical Modeling and Analysis for Spatial Data

✍ Scribed by Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand

• Publisher: Chapman and Hall\/CRC
• Year: 2003
• Tongue: English
• Leaves: 451
• Series: Monographs on Statistics and Applied Probability 101
• Edition: 1
• Category: Library

✦ Synopsis

Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

✦ Table of Contents

Monographs on statistics and applied probability
......Page 1
Title
......Page 4
Copyright
......Page 5
Contents
......Page 7
Preface
......Page 12
1. Overview of spatial data problems
......Page 15
2. Basics of point-referenced data models
......Page 35
3. Basics of areal data models
......Page 83
4. Basics of Bayesian inference
......Page 112
5. Hierarchical modeling for univariate spatial data
......Page 142
6. Spatial misalignment
......Page 188
7. Multivariate spatial modeling
......Page 229
8. Spatiotemporal modeling
......Page 267
9. Spatial survival models
......Page 312
10. Special topics in spatial process modeling
......Page 353
Appendices
......Page 387
Appendix a
......Page 388
Appendix b
......Page 414
References
......Page 432