We review recent research on statistical methods for analysing spatial patterns of points on a
network of lines, such as road accident locations along a road network. Due to geometrical …

This article reviews developments in statistics for spatial point processes obtained within
roughly the past decade. These developments include new classes of spatial point process …

Motivated by the general ability of cross-validation to reduce overfitting and mean square
error, we develop a cross-validation-based statistical theory for general point processes. It is …

The theoretical foundation for a number of model selection criteria is established in the
context of inhomogeneous point processes and under various asymptotic settings: infill …

Existing methods for fitting Neyman–Scott cluster process models to spatial point pattern
data often fail to converge, or converge to implausible values of the parameters, or exhibit …

This paper deals with feature selection procedures for spatial point processes intensity
estimation. We consider regularized versions of estimating equations based on Campbell …

We develop a general approach to spatial inhomogeneity in the analysis of spatial point
pattern data. The ideas of local likelihood (or 'geographically weighted regression') are …

This chapter deals with spatial statistics applied to point patterns. As well as in many
specialized books, spatial point patterns are usually treated elaborately in books devoted to …

We propose a novel tree-based ensemble method, named XGBoostPP, to nonparametrically
estimate the intensity of a point process as a function of covariates. It extends the use of …

Motivated by the analysis of a comprehensive database of road traffic accidents, we
investigate methods of variable selection for spatial point process models on a linear …