000			03354nam a22004335i 4500
001			978-0-387-48536-2
003			DE-He213
005			20250710084002.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387485362 _a99780387485362
024	7		_a10.1007/978-0-387-48536-2 _2doi
100	1		_aDiggle, Peter J. _eauthor.
245	1	0	_aModel-based Geostatistics _h[recurso electrónico] / _cby Peter J. Diggle, Paulo J. Ribeiro.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXIII, 228 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aAn overview of model-based geostatistics -- Gaussian models for geostatistical data -- Generalized linear models for geostatistical data -- Classical parameter estimation -- Spatial prediction -- Bayesian inference -- Geostatistical design.
520			_aGeostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. The authors have written an expository text, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in the book by using the authors' R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models, but does not require previous exposure to spatial statistical models or methods. The authors have used the material in MSc-level statistics courses. Peter Diggle is Professor of Statistics at Lancaster University and Adjunct Professor of Biostatistics at Johns Hopkins University School of Public Health. Paulo Ribeiro is Senior Lecturer at Universidade Federal do Paraná.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aSTATISTICS.
650	1	4	_aGEOSCIENCES.
650	2	4	_aMATH. APPLICATIONS IN GEOSCIENCES.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
700	1		_aRibeiro, Paulo J. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387329079
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-48536-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-EES
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