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Statistical Analysis of Environmental Space-Time Processes (Springer Series in Statistics): Nhu D. Le, James V. Zidek

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.

Summary: Analysis techniques for spatially distributed data

Rating: 4

This is a chatty discussion of mathematical techniques for analyzing sampled spatially distributed data.

I found it quite accessible. It included advances in classical techniques occurring since I last studied geostatistics. The issue of estimating extremes is openly discussed if not completely laid to rest.

Bottom line: I liked it and found it useful.

Table of Contents Pt. IEnvironmental processes 1First encounters3 2Case study15 3Uncertainty27 4Measurement35 5Modeling53 Pt. IISpace-time modeling 6Covariances83 7Spatial prediction : classical approaches101 8Bayesian kriging119 9Hierarchical Bayesian kriging131 Pt. IIIDesign and risk assessment 10Multivariate modeling153 11Environmental network design185 12Extremes215 13Risk assessment245 14R tutorial271

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Rating:
2.5 out of 5 by Book123