Model-Based Geostatistics

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

- Introduction
- 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