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Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geogr...
Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geogr...
Mat\'ern's hard-core processes are valuable point process models in spatial statistics. In order to extend their field of application, Mat\'ern's original models are generalized here, both as point pr...
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an un...
Whenever eye movements are measured, a central part of the analysis has to do withwheresubjects fixate, andwhy they fixated where they fixated. To a first approximation, a set of fixations can be view...
A framework of generalized linear point process models (glppm) much akin to glm for regression is developed where the intensity depends upon a linear predictor process through a known function.In the ...
We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past, and apply these methods in two different contexts. A new version of the algori...
The inspection of residuals is a fundamental step to investigate the quality of adjustment of a parametric model to data. For spatial point processes, the concept of residuals has been recently prop...
The paper deals with homogeneous random point processes in Rn. Our aim is to obtain sufficient conditions for asymptotic normality of the number of points of a point process P in a ball when the ra...
Using the thinning operator there are defined stable and multiply selfdecomposable point processes. A representation for generating functional of such processes is given. Moreover, stable and Poiss...
We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point proces...
Orderliness and compensation for multiparameter point processes
This paper is devoted to the estimation of a vector θ parametrizing an energy function of a Gibbs point process, via the maximum pseudolikelihood method. Strong consistency and asymptotic normality re...

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