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Statistical Analysis of Environmental Space-Time Processes [electronic resource] / by Nhu D. Le, James V. Zidek.

By: Le, Nhu D [author.].
Contributor(s): Zidek, James V [author.] | SpringerLink (Online service).
Material type: TextTextSeries: Springer Series in Statistics: Publisher: New York, NY : Springer New York, 2006Description: XVI, 342 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387354293.Subject(s): Environmental toxicology | Epidemiology | Geography | Mathematical statistics | Monitoring/Environmental Analysis | Ecotoxicology | Epidemiology | Earth Sciences, general | Statistical Theory and MethodsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 363.7063 Online resources: Click here to access online
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.
In: Springer eBooksSummary: This book provides a broad introduction to the fascinating subject of environmental space-time processes; addressing the role of uncertainty. Within that context, it covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. The contents reflect the authors’ cumulative knowledge gained over many years of consulting and research collaboration. In particular, with members of their research group, they developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. This book contains technical and non-technical material and it is written for statistical scientists as well as consultants, subject area researchers and students in related fields. Novel chapters present the authors’ hierarchical Bayesian approaches to spatially interpolating environmental processes designing networks to monitor environmental processes multivariate extreme value theory incorporating risk assessment. In addition, they present a comprehensive and critical survey of other approaches, highlighting deficiencies that their method seeks to overcome. Special sections marked by an asterisk provide rigorous development for readers with a strong technical background. Alternatively readers can go straight to the tutorials supplied in chapter 14 and learn how to apply the free, downloadable modeling and design software that the authors and their research partners have developed. Nhu Le is a Senior Scientist in Cancer Control Research and a former Director of the Occupational Oncology Research Program at the British Columbia Cancer Agency (BCCA). An Adjunct Professor of Statistics at the University of British Columbia since 1992, he also teaches graduate courses and supervises graduate students. He is heavily involved in epidemiological research and the impact environmental and occupational factors have on cancer development. He has published over 100 peer-reviewed research articles in statistical- and subject-area journals. He received his Ph.D. in statistics from the University of Washington in Seattle. Jim Zidek is a Professor Emeritus and Founding Head of the Department of Statistics at the University of British Columbia. He has served on a number of scientific advisory committees, most notably on the United States’ EPA’s Clean Air Scientific Advisory Committees Ozone Panel. His scientific interests lie equally in environmetrics (the subject of this book) and in the theory of decision analysis (particularly, the compilation of expert opinion). His work has been published extensively and he has been invited to give numerous presentations. He received his Ph.D. from Stanford University and his honors include Fellowships in the Royal Society of Canada, the American Statistical Association (ASA), and the Institute of Mathematical Statistics. He has earned the Distinguished Achievement Medal in Environmental Statistics of the ASA and the Gold Medal of the Statistical Society of Canada.
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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.

This book provides a broad introduction to the fascinating subject of environmental space-time processes; addressing the role of uncertainty. Within that context, it covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. The contents reflect the authors’ cumulative knowledge gained over many years of consulting and research collaboration. In particular, with members of their research group, they developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. This book contains technical and non-technical material and it is written for statistical scientists as well as consultants, subject area researchers and students in related fields. Novel chapters present the authors’ hierarchical Bayesian approaches to spatially interpolating environmental processes designing networks to monitor environmental processes multivariate extreme value theory incorporating risk assessment. In addition, they present a comprehensive and critical survey of other approaches, highlighting deficiencies that their method seeks to overcome. Special sections marked by an asterisk provide rigorous development for readers with a strong technical background. Alternatively readers can go straight to the tutorials supplied in chapter 14 and learn how to apply the free, downloadable modeling and design software that the authors and their research partners have developed. Nhu Le is a Senior Scientist in Cancer Control Research and a former Director of the Occupational Oncology Research Program at the British Columbia Cancer Agency (BCCA). An Adjunct Professor of Statistics at the University of British Columbia since 1992, he also teaches graduate courses and supervises graduate students. He is heavily involved in epidemiological research and the impact environmental and occupational factors have on cancer development. He has published over 100 peer-reviewed research articles in statistical- and subject-area journals. He received his Ph.D. in statistics from the University of Washington in Seattle. Jim Zidek is a Professor Emeritus and Founding Head of the Department of Statistics at the University of British Columbia. He has served on a number of scientific advisory committees, most notably on the United States’ EPA’s Clean Air Scientific Advisory Committees Ozone Panel. His scientific interests lie equally in environmetrics (the subject of this book) and in the theory of decision analysis (particularly, the compilation of expert opinion). His work has been published extensively and he has been invited to give numerous presentations. He received his Ph.D. from Stanford University and his honors include Fellowships in the Royal Society of Canada, the American Statistical Association (ASA), and the Institute of Mathematical Statistics. He has earned the Distinguished Achievement Medal in Environmental Statistics of the ASA and the Gold Medal of the Statistical Society of Canada.

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Statistical analysis of environmental space-time processes by Le Nhu D
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