Spatial Statistics and Modeling

160,49 €
inkl. 7% MwSt. und
ggf. zzgl. Versand

<P>Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environmental and earth sciences, epidemiology, image analysis and more. This book covers the best-known spatial models for three types of spatial data: geostatistical data&nbsp; (stationarity, intrinsic models, variograms, spatial regression and space-time models), areal data&nbsp; (Gibbs-Markov fields and spatial auto-regression) and point pattern data (Poisson, Cox, Gibbs and Markov point processes). The level is relatively advanced, and the presentation concise but complete.</P><P>&nbsp;The most important statistical methods and their asymptotic&nbsp; properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models. A chapter is devoted to Markov Chain Monte Carlo simulation (Gibbs sampler, Metropolis-Hastings algorithms and exact simulation).<BR>A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises. While a foundation in&nbsp; probability and mathematical statistics is assumed,&nbsp; three appendices introduce some necessary background. The book is accessible to senior undergraduate students with a solid math background&nbsp; and Ph.D. students in statistics. Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference.</P><P>This book is the English translation of Modélisation et Statistique Spatiales published by Springer in the series Mathématiques &amp; Applications, a series established by Société de Mathématiques Appliquées et Industrielles (SMAI).</P>