Statistical Analysis Of Environmental Space-Time Processes

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Le, Nhu D., Zidek, James V.

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ISBN:9780387262093
Editorial: Springer Verlag
Fecha de la edición:2006
Colección: Springer Series in Statistics
Encuadernación:Tapa dura
Dimensiones: 0 cm x 0 cm
Nº Pág.:356
Idiomas:Ingles,Inglés

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/ Economía Cuantitativa

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.

First encounters.- Case study.- Uncertainty.- Measurement.- Modelling.- Covariances.- Classical approaches.- Bayesian kriging.- Hierarchical methods.- Multivariate modelling.- Network design.- Extremes.- Risk assessment.- R tutorial.