Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. Model-Based Geostatistics is the first book-length treatment of model-based geostatistics.
The authors have written an expository text, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in Model-Based Geostatistics by using the authors' R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter.
Model-Based Geostatistics assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models, but does not require previous exposure to spatial statistical mode
"This is one of the first books to provide examples on how to use GeoR and for that reason alone is an excellent book for practitioners of geostatistics."
- Ashok K. Singh, Technometrics, August 2009, Vol. 51, No. 3
"[...] [T]his book provides a very good insight into the field of model-based geostatistics. The authors succeed in getting the reader through the various stages and methods of analyzing geostatistical data. The book can be recommended to all who are interested in model-based approaches for the analysis of geostatistical data."
- Daniela Gumprecht, Journal of Statistical Sofware, Vol. 21, September 2007
"The current book aims to give an introduction and an overview of model-based geostatistics, including some applications using geoR. [...] It is an excellent book for graduate students. It reaches the audience and its inventions very well. It is rich in contents, both in terms of statistical depth, in the range of applications as well as in access and use of software."
- A. Stein, Kwantitatieve Methoden, October, 2007
"This volume consists of eight chapters and an appendix. It is clearly intended for graduate students in statistics and to a lesser extent those simply using geostatistics. Each chapter has exercises which are a mix of applied and theoretical, the applied exercises often using one of the associated R packages. [...] This volume is an important contribution to the literature [...]"
- Donald E. Myers, SIAM Review, Vol. 50 (1), 2008
"The authors of this book describe an approach to geostatistical problems based on the application of formal statistical methods under an explicitly assumed stochastic model. This approach is called model-based geostatistics. [...] The intended readership includes postgraduate statistics students and scientific researchers whose work involves the analysis of geostatistical data. [...] this book is a spirited performance and can be recommended for anyone interested in geostatistics."
- Wolfgang Nather, Zentralblatt MATH, Vol. 1132 (10), 2008
"Model-Based Geostatistics is appropriate as a textbook for applied geostatistics or as supportive material for spatial statistics for graduate students. [...] Overall, this book provides a comprehensive summary of model-based geostatitics. It is easy to follow even without a very strong statistical background. [...] In addition, the book offered at a reasonable price. I strongly recommend that this book be on the shelf of all researchers, scientists, and graduate students who are interested in or currently working on geostatistical data."
- Bo Li, Journal of the American Statistical Association, Vol. 103 (483), September, 2008
- An Overview of Model-Based Geostatistics
- Gaussian Models for Geostatistical Data
- Generalized Linear Models for Geostatistical Data
- Classical Parameter Estimation
- Spatial Prediction
- Bayesian Inference
- Geostatistical Design
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Peter Diggle is Professor of Statistics at Lancaster University and Adjunct Professor of Biostatistics at Johns Hopkins University School of Public Health. Paulo Ribeiro is Senior Lecturer at Universidade Federal do Paraná.