50 citations · 71 across the 18 of their papers we have counts for
13 papers · 1 filter
A covariance density for a point process and a random field
Markus Frederik Palmelund, Ib Thorsgaard Jensen, Rasmus Waagepetersen
The spatial point process literature offers a well-developed theory for characterizing covariances within a point process or between different point processes using joint intensity…
-functions for point processes on complex surfaces
Francisco Cuevas-Pacheco, Scott Ward, Ed Cohen +1
The -function is a fundamental summary statistic for assessing clustering or regularity of point processes in two or three dimensional Euclidean space. In practice, however, man…
Semi-parametric Markov models for multi-type point patterns
Ib Thorsgaard Jensen, Jean-François Coeurjolly, Rasmus Waagepetersen
Multi-type Markov point processes offer a flexible framework for modelling complex multi-type point patterns where it is pertinent to capture both interactions between points as we…
Composite likelihood inference for space-time point processes
Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu +1
The dynamics of a rain forest is extremely complex involving births, deaths and growth of trees with complex interactions between trees, animals, climate, and environment. We consi…
A -function for inhomogeneous random measures with geometric features
Anne Marie Svane, Hans Jacob Teglbjærg Stephensen, Rasmus Waagepetersen
This paper introduces a -function for assessing second-order properties of inhomogeneous random measures generated by marked point processes. The marks can be geometric objects…
Currents and K-functions for Fiber Point Processes
Pernille EH. Hansen, Rasmus Waagepetersen, Anne Marie Svane +4
Analysis of images of sets of fibers such as myelin sheaths or skeletal muscles must account for both the spatial distribution of fibers and differences in fiber shape. This necess…
Second order semi-parametric inference for multivariate log Gaussian Cox processes
Kristian Bjørn Hessellund, Ganggang Xu, Yongtao Guan +1
This paper introduces a new approach to inferring the second order properties of a multivariate log Gaussian Cox process (LGCP) with a complex intensity function. We assume a semi-…
Globally intensity-reweighted estimators for - and pair correlation functions
Thomas Shaw, Jesper Møller, Rasmus Waagepetersen
We introduce new estimators of the inhomogeneous -function and the pair correlation function of a spatial point process as well as the cross -function and the cross pair corr…
Regularized estimation for highly multivariate log Gaussian Cox processes
Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly +1
Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable…
Second-order variational equations for spatial point processes with a view to pair correlation function estimation
Jean-François Coeurjolly, Francisco Cuevas-Pacheco, Rasmus Waagepetersen
Second-order variational type equations for spatial point processes are established. In case of log linear parametric models for pair correlation functions, it is demonstrated that…
Generalizations of Ripley's K-function with Application to Space Curves
Jon Sporring, Rasmus Waagepetersen, Stefan Sommer
The intensity function and Ripley's K-function have been used extensively in the literature to describe the first and second moment structure of spatial point sets. This has many a…
Some recent developments in statistics for spatial point patterns
Jesper Møller, Rasmus Waagepetersen
This paper reviews developments in statistics for spatial point processes obtained within roughly the last decade. These developments include new classes of spatial point process m…
Quasi-likelihood for Spatial Point Processes
Yongtao Guan, Abdollah Jalilian, Rasmus Waagepetersen
Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referen…