Model-based Geostatistics (Springer Series in Statistics)

I was very interested in "Model-based Geostatistics" by Diggle and Ribeiro because I teach a course in applied geostatistics. The book was informative. The preface was interesting read because most of the geostatistics of which I am familiar is based upon the work of Matheron. I was unaware that the Matheron work was "developed largely independently of the mainstream of spatial geostatistics." Topics that were of interest to me were ones such as 2.3 Exploratory data analysis. This concept is often not emphasized enough. Another interesting section was 6.4 What does Kriging actually do to the data? Section 8.1 Choosing the study region was interesting, but, as the authors state "...is often pre-determined by the context of the investigation...." Choosing the sample locations: Uniform designs (8.2) was another interesting section. 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. This volume 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 the book by using the authors'R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book 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 models or methods. The authors have used the material in MSc-level statistics courses. 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. This volume 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 the book by using the authors' R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book 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 models or methods. The authors have used the material in MSc-level statistics courses This Volume Is The First Book-length Treatment Of Model-based Geostatistics. The Text Is Expository, 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 The Book By Using The Authors' Software Package, Geor, Whose Usage Is Illustrated In A Computation Section At The End Of Each Chapter. The Book Assumes A Working Knowledge Of Classical And Bayesian Methods Of Inference, Linear Models, And Generalized Linear Models. Introduction -- An Overview Of Model-based Statistics -- Gaussian Models For Geostatistical Data -- Generalized Linear Models For Geostatistical Data -- Classical Parameter Estimation -- Spatial Prediction -- Bayesian Inference -- Geostatistical Design -- A Statistical Background. Peter J. Diggle, Paulo J. Ribeiro, Jr. Includes Bibliographical References (p. [218]-226) And Index. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume provides a treatment of model-based geostatistics and emphasizes on statistical methods and applications. It also features analyses of datasets from a range of scientific contexts