000 04026nam a22005415i 4500
001 978-0-387-92257-7
003 DE-He213
005 20140220084456.0
007 cr nn 008mamaa
008 100301s2010 xxu| s |||| 0|eng d
020 _a9780387922577
_9978-0-387-92257-7
024 7 _a10.1007/978-0-387-92257-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aGaetan, Carlo.
_eauthor.
245 1 0 _aSpatial Statistics and Modeling
_h[electronic resource] /
_cby Carlo Gaetan, Xavier Guyon.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Statistics,
_x0172-7397
505 0 _aSecond-order spatial models and geostatistics -- Gibbs-Markov random fields on networks -- Spatial point processes -- Simulation of spatial models -- Statistics for spatial models.
520 _aSpatial 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 (stationarity, intrinsic models, variograms, spatial regression and space-time models), areal data (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. The most important statistical methods and their asymptotic 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). 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 probability and mathematical statistics is assumed, three appendices introduce some necessary background. The book is accessible to senior undergraduate students with a solid math background 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. This book is the English translation of Modélisation et Statistique Spatiales published by Springer in the series Mathématiques & Applications, a series established by Société de Mathématiques Appliquées et Industrielles (SMAI). Carlo Gaetan is Associate Professor of Statistics in the Department of Statistics at the Ca' Foscari University of Venice. Xavier Guyon is Professor Emeritus at the University of Paris 1 Panthéon-Sorbonne. He is author of a Springer monograph on random fields.
650 0 _aStatistics.
650 0 _aMathematical geography.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 0 _aEnvironmental sciences.
650 0 _aEconometrics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aMathematical Applications in Earth Sciences.
650 2 4 _aEconometrics.
650 2 4 _aMath. Appl. in Environmental Science.
700 1 _aGuyon, Xavier.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387922560
830 0 _aSpringer Series in Statistics,
_x0172-7397
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-92257-7
912 _aZDB-2-SMA
999 _c109865
_d109865