On parameter estimation for pairwise interaction point processes

By Peter J. Diggle, T. Fiksel, P. Grabarnik, Yosihiko Ogata, Dietrich Stoyan, M. Tanemura. 1994

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, methodology): “…pseudolikelihood [Besag, 1977, Besag et al., 1982, Jensen and Moller, 1991, Ripley, 1991], Markov chain Monte Carlo methods [Ripley and Kelly, 1977, Besag and Green, 1993, Häggström et al., 1999, Berthelsen and Møller, 2006], and other approximations [Ogata and Tanemura, 1985, Diggle et al., 1994].”
Global envelope tests for spatial processes (2013, methodology): “ed on the pair correlation function (the non-cumulative counterpart of the L-function), whereas for the Strauss process, we used the maximum pseudo-likelihood (MPL) method (Baddeley and Turner, 2000; Diggle et al., 1994) (default method in spatstat) and the logistic likelihood method (Baddeley et al”
Area-interaction point processes (1993, methodology): “We show that pseudolikelihood inference for the area-interaction process is a special ca,se of the Takacs-Fiksel method, analogous to the situation for pairwise interaction processes (Diggle et al. (1994), SSrkk'a (1989), Ripley (1989)).”
Explosive connections? Mass media, social media, and the geography of collective violence in African states (2015, background): “…means of accounting for the possibility that events are more likely to arise in proximity to other events, within a specified interaction radius (Diggle et al., 1994; Waagepetersen, 2007; Strauss, 1975).8 Optimized coefficients and standard errors for the parameters governing the strength of…”
A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns (2000, methodology): “Our third model, previously used by Ogata and Tanemura (1984) and Diggle et al. (1994), has interaction function e(t) = 1 − exp{−(t/θ)2}.”