Estimating functions for inhomogeneous spatial point processes with incomplete covariate data

51 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Logistic regression for spatial Gibbs point processes (2014, background): “Spatial Gibbs and Markov point processes are important classes of models for spatial depen- 25 dence in point patterns (van Lieshout, 2000) with a broad range of applications (e.g. Harkness & Isham, 1983; Stoyan & Penttinen, 2000; Mattfeldt et al., 2007; Funwi-Gabga & Mateu, 2012).”
Handbook of Spatial Statistics (2008, unknown): “This covariate may only be partially observed on a grid of points, and hence some interpolation technique may be needed (Rathbun, 1996; Rathbun, Shiffman and Gwaltneyet, 2007; Waagepetersen, 2008).”
Spatial logistic regression and change-of-support in Poisson point processes (2010, background): “More generally Waagepetersen [76] studied estimating functions of the form UW (β) = ∑ xi∈X Z(xi)− ∑ uj∈X∪S Z(uj) λβ(uj) s(uj) (63) where s(uj) is a suitable function.”