基本説明
Presents a middle ground on theory and applicability through non-mathematical discussion in each chapter for non-statisticians, software availability, and detailed instructions on its use in real applications.
Full Description
This book presents knowledge gained by the authors along with methods they developed, over more than 30 years of experience measuring, mod- ing, and mapping environmental space-time ?elds. That experience embraces both large (continentwide) spatial domains and small. In part it comes from their research, working with students as well as coinvestigators. But much was gained from all sorts of interactions with many individuals who have had to contend with the challenges these ?elds present. They include statistical as well as subject area scientists, in areas as diverse as analytical chemistry, airsampling,atmosphericscience,environmentalepidemiology,environmental risk management, and occupational health among others. We have colla- rated and consulted with government scientists as well as policy-makers, in all,alargegroupofindividualsfromwhomwehavelearnedalotandtowhom we are indebted. We hope all in these diverse groups will ?nd something of value in this book. We believe it will also bene?t graduate students, both in statistics and subject areas who must deal with the analysis of environmental ?elds. In fact we have given a successful statistics graduate course based on it. The book (and course) re?ect our conviction about the need for statistical scientiststolearnaboutthephenomenatheypurporttoexplain. Totheextent feasible, we have covered important nonstatistical issues involved in dealing with environmental processes. Thus in writing the book we have tried to strike a balance between important qualitative and quantitative aspects of the subject. Much of the most technical statistical-mathematical material has been placed in the starred sections, chapters, and appendices.
Contents
Environmental Processes.- First Encounters.- Case Study.- Uncertainty.- Measurement.- Modeling.- Space-Time Modeling.- Covariances.- Spatial Prediction: Classical Approaches.- Bayesian Kriging.- Hierarchical Bayesian Kriging.- Design and Risk Assessment.- Multivariate Modeling***.- Environmental Network Design.- Extremes.- Implementation.- Risk Assessment.- A Tutorial in R.- Appendices.- References.
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