Modern Statistics for Spatial Point Processes *

By Jesper Møller, Rasmus Waagepetersen. 2007

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In Applied Mathematics

Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “The Gibbs point process provides such an approach, offering a general framework for incorporating correlations among selected items [Preston, 1976, Ripley and Kelly, 1977, Ripley, 1991, Van Lieshout, 2000, Møller and Waagepetersen, 2004, 2007, Daley and Vere-Jones, 2008].”
Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, methodology): “We now fit a HPP and a Strauss model to the Swedish pines data from Ripley (1981) and Baddeley and Turner (2000).”
Properties of residuals for spatial point processes (2008, methodology): “In practice it may be preferable to replace λ(u, XW) by the intensity function λ(u) = E[λ(u, XW)] and to use only the raw residuals, as mooted in Møller and Waagepetersen (2007).”
Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013, methodology): “In spatial statistics, the multiobject posterior encapsulates the spatial distribution of point patterns as well as relevant statistics [5], [6], and is often approximated via Markov Chain Monte Carlo simulation [7].”
A J-function for inhomogeneous point processes (2010, methodology): “See also the examples of Cox processes in Møller and Waagepetersen (2007).”
Lévy-based Cox point processes (2008, background): “In the very recent review paper [32], a tropical rain forest is modeled by inhomogeneous shot noise Cox processes, obtained by thinning of a homogeneous shot noise Cox process with a log-linear deterministic field depending on explanatory variables.”
Fitting complex ecological point process models with integrated nested Laplace approximation (2013, methodology): “We exploit this here and focus on log-Gaussian Cox processes, as considered inMøller, Syversveen &Waagepetersen (1998) and Møller & Waagepetersen (2004, 2007).”
Structured Spatio‐Temporal Shot‐Noise Cox Point Process Models, with a View to Modelling Forest Fires (2010, methodology): “See Møller and Waagepetersen (2007) for the case of a single spatial point process, which easily extend to our spatio-temporal case.”
Estimating functions for inhomogeneous spatial point processes with incomplete covariate data (2008, background): “However, (1) may still be used as an estimating function for regression parameters in certain inhomogeneous cluster processes, see Waagepetersen (2006) and Møller and Waagepetersen (2006). In practice z(·) is often only observed at a finite set of locations so that the integral in (1) cannot be eval”
Joint Detection and Estimation of Multiple Objects From Image Observations (2010, background): “In spatial statistics applications, for example, agriculture and forestry, it is of interest to study the underlying spatial distribution of points (plants) from partial observations such as aerial images [8], [9], [14].”