| 000 | 05287nam a22005175i 4500 | ||
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| 001 | 978-0-387-31144-9 | ||
| 003 | DE-He213 | ||
| 005 | 20161121231025.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2006 xxu| s |||| 0|eng d | ||
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_a9780387311449 _9978-0-387-31144-9 |
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| 024 | 7 |
_a10.1007/0-387-31144-0 _2doi |
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| 050 | 4 | _aQA276-280 | |
| 072 | 7 |
_aPBT _2bicssc |
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| 072 | 7 |
_aMAT029000 _2bisacsh |
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| 082 | 0 | 4 |
_a519.5 _223 |
| 245 | 1 | 0 |
_aCase Studies in Spatial Point Process Modeling _h[electronic resource] / _cedited by Adrian Baddeley, Pablo Gregori, Jorge Mateu, Radu Stoica, Dietrich Stoyan. |
| 264 | 1 |
_aNew York, NY : _bSpringer New York, _c2006. |
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| 300 |
_aXVIII, 310 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aLecture Notes in Statistics, _x0930-0325 ; _v185 |
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| 505 | 0 | _aBasic Notions and Manipulation of Spatial Point Processes -- Fundamentals of Point Process Statistics -- Modelling Spatial Point Patterns in R -- Theoretical and Methodological Advances in Spatial Point Processes -- Strong Markov Property of Poisson Processes and Slivnyak Formula -- Bayesian Analysis of Markov Point Processes -- Statistics for Locally Scaled Point Processes -- Nonparametric Testing of Distribution Functions in Germ-grain Models -- Principal Component Analysis for Spatial Point Processes — Assessing the Appropriateness of the Approach in an Ecological Context -- Practical Applications of Spatial Point Processes -- On Modelling of Refractory Castables by Marked Gibbs and Gibbsian-like Processes -- Source Detection in an Outbreak of Legionnaire’s Disease -- Doctors’ Prescribing Patterns in the Midi-Pyrénées rRegion of France: Point-process Aggregation -- Strain-typing Transmissible Spongiform Encephalopathies Using Replicated Spatial Data -- Modelling the Bivariate Spatial Distribution of Amacrine Cells -- Analysis of Spatial Point Patterns in Microscopic and Macroscopic Biological Image Data -- Spatial Marked Point Patterns for Herd Dispersion in a Savanna Wildlife Herbivore Community in Kenya -- Diagnostic Analysis of Space-Time Branching Processes for Earthquakes -- Assessing Spatial Point Process Models Using Weighted K-functions: Analysis of California Earthquakes. | |
| 520 | _aPoint process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples. Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many well-founded point process case studies. Adrian Baddeley is Professor of Statistics at the University of Western Australia (Perth, Australia) and a Fellow of the Australian Academy of Science. His main research interests are in stochastic geometry, stereology, spatial statistics, image analysis and statistical software. Pablo Gregori is senior lecturer of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon. His research fields of interest are spatial statistics, mainly on spatial point processes, and measure theory of functional analysis. Jorge Mateu is Assistant Professor of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon and a Fellow of the Spanish Statistical Society and of Wessex Institute of Great Britain. His main research interests are in stochastic geometry and spatial statistics, mainly spatial point processes and geostatistics. Radu Stoica obtained his Ph.D. in 2001 from the University of Nice Sophia Anitpolis. He works within the biometry group at INRA Avignon. His research interests are related to the study and the simulation of point processes applied to pattern modeling and recognition. The aimed application domains are image processing, astronomy and environmental sciences. Dietrich Stoyan is Professor of Applied Stochastics at TU Bergakademie Freiberg, Germany. Since the end of the 1970s he has worked in the fields of stochastic geometry and spatial statistics. | ||
| 650 | 0 | _aStatistics. | |
| 650 | 0 | _aEarth sciences. | |
| 650 | 0 | _aProbabilities. | |
| 650 | 1 | 4 | _aStatistics. |
| 650 | 2 | 4 | _aStatistical Theory and Methods. |
| 650 | 2 | 4 | _aProbability Theory and Stochastic Processes. |
| 650 | 2 | 4 | _aEarth Sciences, general. |
| 700 | 1 |
_aBaddeley, Adrian. _eeditor. |
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| 700 | 1 |
_aGregori, Pablo. _eeditor. |
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| 700 | 1 |
_aMateu, Jorge. _eeditor. |
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| 700 | 1 |
_aStoica, Radu. _eeditor. |
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| 700 | 1 |
_aStoyan, Dietrich. _eeditor. |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9780387283111 |
| 830 | 0 |
_aLecture Notes in Statistics, _x0930-0325 ; _v185 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/0-387-31144-0 |
| 912 | _aZDB-2-SMA | ||
| 950 | _aMathematics and Statistics (Springer-11649) | ||
| 999 |
_c507441 _d507441 |
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