Peter Diggle, Paulo Justiniano Ribeiro

eBook, PDF

Model-based Geostatistics (eBook, PDF)

Beschreibung

This volume is the first book-length treatment of 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. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. 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.

Technische Details

• Geräte: PC
• ohne Kopierschutz
• Größe: 12.22MB

Barrierefreiheit

• Text-to-Speech

Autor

Peter J. Diggle, University of Lancaster, UK / Paulo Justiniano Ribeiro, Universidade Federal do Paraná, Curitiba, Brazil

Produktdetails

• Verlag: Springer US
• Seitenzahl: 232
• Erscheinungstermin: 26. Mai 2007
• Englisch
• ISBN-13: 9780387485362
• Artikelnr.: 37287527

Rezensionen

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. âEUR¿ 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. âEUR¿ This volume is an important contribution to the literature âEUR¿ ." (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. âEUR¿ The intended readership includes postgraduate statistics students and scientific researchers whose work involves the analysis of geostatistical data. âEUR¿ this book is a spirited performance and can be recommended for anyone interested in geostatistics." (Wolfgang Nÿther, 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. âEUR¿ Overall, this book provides a comprehensive summary of model-based geostatitics. It is easy to follow even without a very strong statistical background. âEUR¿ 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)