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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.
- Sales Rank: #1314730 in Books
- Published on: 2007-03-12
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .63" w x 6.14" l, 1.04 pounds
- Binding: Hardcover
- 228 pages
Review
From the reviews:
"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 Näther, 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)
From the Back Cover
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.
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 Paran�.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Direct and Practical
By Rodrigo Aluizio
There's no better way to learn something, but practicing. This concise and direct book shows you what you must know, who you may apply it and the most important of all, how you can do it using R statistical software (an amazing tool by the way). Geostatistics may be tricky sometimes, and as all the statistics you must be sure you are doing the right thing the right way. This book you help you in this task just the way you need.
5 of 7 people found the following review helpful.
Supplemental text
By R. E. Zartman
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.
0 of 2 people found the following review helpful.
Everything was perfect!
By Felipe Torquato
The book was shipped as soon as possible. Everything was perfect!
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