Astrostatistics Image Penn State University Eberly College of Science Center for Astrostatistics Center for Astrostatistics

 
CASt online resources

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.

Return to CASt bibliographies