Multivariate Geostatistical Models - Inference and Computation

Multivariate geostatistical data involves the observation of two or more spatial processes at spatial and/or temporal points. This book covers key topics in this area, including modeling and estimating the multivariate covariogram, multivariate spatial generalized linear mixed models, and multivariate non-Gaussian models.

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Summary

Multivariate geostatistical data involves the observation of two or more spatial processes at spatial and/or temporal points. The objective of such analysis is to estimate the correlation or dependence structure between the multiple variable or to predict the multiple variable at unsampled locations. Multivariate Geostatistical Models provides complete coverage of all key topics in this area, including modeling and estimating the multivariate covariogram, multivariate spatial generalized linear mixed models, and multivariate non-Gaussian models. Illustrated with detailed worked examples from a range of disciplines, the book implements methods using Markov chain Monte Carlo (MCMC) simulation for Bayesian inference.

Product details

• Authors: Hao Zhang, Zhang Zhang
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 25.05.2012
• EAN: 9781584886990
• ISBN: 978-1-58488-699-0
• No. of pages: 320
• Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability; Chapman & Hall/CRC Monographs on Statistics & Applied Probability; Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Subject: Natural sciences, medicine, IT, technology > Biology