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Spatial
Point Processes
- Statistical
Analysis of Spatial Point Patterns
by Peter Diggle (2nd ed, 2001). A short, classic
monograph
on the analysis of spatial data with applications to environmental and
epidemiological research. Topics include sparsely sampled
patterns, summary statistics, likelihood-based modelling, and
nonparametric methods.
- Statistics
for Spatial Data
by Noel A. Cressie (1993). Comprehensive monograph on
point
patterns and lattice data. Includes introduction to
geostatistics, kriging (ordinary, universal, robust, Bayesian, etc)
& variograms, spatial prediction, inference for lattice models
(Markov random fields, parameteri estimation), spatial randomness,
Poisson & Cox processes, multivariate & marked spatial point
processes.
- Statistical
Inference and Simulation for Spatial Point Processes
by Jesper Moller, R. P. Waagepetersen (2003). Graduate-level
monograph on point patterns with emphasis on MCMC methods. Covers
Poisson point processes, summary statistics (K, L, F, G and J
functions), Cox processes, Markov point processes, birth-and-death
processes, Metropolis-Hastings algorithms, and
maximum-likelihood,
Bayesian and simulation-based inference.
- Hierarchical
Modeling and Analysis for Spatial Data
by Sudipto Banerjee, Bradley P. Carlin & Alan E.
Gelfand
(2004). Advanced up-to-date text on modeling point process, areal
and map data with some examples using R & WinBUGS. Includes
kriging & variograms, autoregressive models, stationary &
nonstationary models, Bayesian inference, spatial misalighment,
multivariate & spatio-temporal models, spatial survival models.
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