14 pagesLasso and Dantzig selector are standard procedures able to perform variable selection and estimation simultaneously. This paper is concerned with extending these procedures to spatial point process intensity estimation. We propose adaptive versions of these procedures, develop efficient computational methodologies and derive asymptotic results for a large class of spatial point processes under the setting where the number of parameters, i.e. the number of spatial covariates considered, increases with the volume of the observation domain. Both procedures are compared theoretically and in a simulation study
International audienceAssume that several competing methods are available to estimate a parameter in...
Spatial point pattern data are routinely encountered. A flexible regression model for the underlying...
Point processes describe random point patterns in space. One of their most important characteristics...
Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation...
Les applications récentes telles que les bases de données forestières impliquent des observations de...
Recent applications such as forestry datasets involve the observations of spatial point pattern data...
ABSTRACT. This article introduces a kernel estimator of the intensity function of spatial point proc...
International audienceMany methods for estimating parametrically the intensity function for inhomoge...
International audienceThis paper deals with feature selection procedures for spatial point processes...
Abstract. Estimation of the intensity function of spatial point processes is a fundamental problem. ...
International audienceWe introduce a new variational estimator for the intensity function of an inho...
The conditional intensity function of a spatial point process describes how the probability that a p...
We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a...
In the statistical analysis of spatial point patterns, it is often important to investigate whether ...
The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a re...
International audienceAssume that several competing methods are available to estimate a parameter in...
Spatial point pattern data are routinely encountered. A flexible regression model for the underlying...
Point processes describe random point patterns in space. One of their most important characteristics...
Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation...
Les applications récentes telles que les bases de données forestières impliquent des observations de...
Recent applications such as forestry datasets involve the observations of spatial point pattern data...
ABSTRACT. This article introduces a kernel estimator of the intensity function of spatial point proc...
International audienceMany methods for estimating parametrically the intensity function for inhomoge...
International audienceThis paper deals with feature selection procedures for spatial point processes...
Abstract. Estimation of the intensity function of spatial point processes is a fundamental problem. ...
International audienceWe introduce a new variational estimator for the intensity function of an inho...
The conditional intensity function of a spatial point process describes how the probability that a p...
We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a...
In the statistical analysis of spatial point patterns, it is often important to investigate whether ...
The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a re...
International audienceAssume that several competing methods are available to estimate a parameter in...
Spatial point pattern data are routinely encountered. A flexible regression model for the underlying...
Point processes describe random point patterns in space. One of their most important characteristics...