Handbook of Spatial Statistics

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advanced statistical modeling
areal unit statistics
Bounded Borel Set
carlo
Category=PBT
chain
Conditional Intensity
continuous spatial variation
covariance
Covariance Functions
Cox Process
discrete spatial variation
disease mapping
Ecological Fallacy
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
function
gaussian
Gaussian MRFs
Gaussian Process
geostatistical methods
geostatistics
hierarchical modeling
Homogeneous Poisson Process
Increasing Domain Asymptotic
lattice models
Marked Point Patterns
Marked Point Process
markov
monte
Monte Carlo Test
multivariate spatial process models
Nugget Effect
point
Point Pattern
Point Process
Point Process Models
point referenced data
poisson
Poisson Cluster Process
Poisson Process
Posterior Predictive Distribution
Precision Matrix
process
Random Labeling
Smoothness Parameter
spatial aggregation
spatial data analysis
spatial econometrics
spatial gradients
Spatial Point Patterns
Spatial Point Process
spatial statistics
spatial statistics reference for researchers
spatio-temporal modeling
spatio-temporal processes
Vice Versa
wombling

Product details

  • ISBN 9781420072877
  • Weight: 1310g
  • Dimensions: 174 x 246mm
  • Publication Date: 19 Mar 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters.

The handbook begins with a historical introduction detailing the evolution of the field. It then focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and spatial point patterns. The book also contains a section on space–time work as well as a section on important topics that build upon earlier chapters.

By collecting the major work in the field in one source, along with including an extensive bibliography, this handbook will assist future research efforts. It deftly balances theory and application, strongly emphasizes modeling, and introduces many real data analysis examples.

Alan E. Gelfand, Department of Statistical Science, Duke University, Durham, North Carolina, USA

Peter J. Diggle, School of Health and Medicine, Lancaster University, UK

Montserrat Fuentes, Department of Statistics, North Carolina State University, Raleigh, USA

Peter Guttorp, Department of Statistics, University of Washington, Seattle, USA, and Norwegian Computing Center, Oslo, Norway