Properties of residuals for spatial point processes

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Logistic regression for spatial Gibbs point processes (2014, background): “Each component of the vector T1,Wn(X) is a special case of innovations for spatial Gibbs point processes introduced by Baddeley et al. (2005) with variances studied by Baddeley et al. (2008) and asymptotic results225 provided by Coeurjolly & Lavancier (2013) and Coeurjolly & Rubak (2013).”
Fractals and spatial statistics of point patterns (2013, unknown): “Mathematical statisticians (Baddeley and Turner, 2012; Baddeley et al., 2008; Baddeley, 2007; Cressie, 2001; Rowlingson and Diggle, 1993, 1991; Ripley, 1988, 1981, 1976; Diggle, 1983) have developed very precise methods to estimate the parameters of constant-density point processes.”
Modern Statistics for Spatial Point Processes * (2007, methodology): “We therefore use the border method as follows (see also Baddeley et al., 2007a).”
Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, methodology): “Finally, for an h-scaled innovation as in (11), Baddeley et al. (2005) define the smoothed innovation field r(u;θ) at location u ∈ D as”