This article introduces a kernel estimator of the intensity function of spatial point processes taking into account location errors. The asymptotic properties of the estimator are derived and a bandwidth selection procedure is described. A simulation study compares our results with that of the classical kernel estimator and shows that the edge-corrected deconvoluting kernel estimator is more appropriate. Copyright (c) Board of the Foundation of the Scandinavian Journal of Statistics 2008.
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
14 pagesLasso and Dantzig selector are standard procedures able to perform variable selection and es...
International audienceThis paper deals with feature selection procedures for spatial point processes...
ABSTRACT. This article introduces a kernel estimator of the intensity function of spatial point proc...
We investigate the asymptotic mean squared error of kernel estimators of the intensity function of a...
We discuss and compare various approaches to the problem of bandwidth selection for kernel estimato...
We propose a new bandwidth selection method for kernel estimators of spatial point process intensity...
We propose a new bandwidth selection method for kernel estimators of spatial point process intensity...
We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a...
We investigate the asymptotic mean squared error of kernel estimators of the intensity function of ...
Abstract. Estimation of the intensity function of spatial point processes is a fundamental problem. ...
Point processes describe random point patterns in space. One of their most important characteristics...
Point processes are random local finite sets of points in a space that are used for mod- elling and ...
Dealing with data coming from a space-time inhomogeneous process, there is often the need of semi-pa...
International audienceThis paper is concerned with the problem of estimating covariances of inhomoge...
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
14 pagesLasso and Dantzig selector are standard procedures able to perform variable selection and es...
International audienceThis paper deals with feature selection procedures for spatial point processes...
ABSTRACT. This article introduces a kernel estimator of the intensity function of spatial point proc...
We investigate the asymptotic mean squared error of kernel estimators of the intensity function of a...
We discuss and compare various approaches to the problem of bandwidth selection for kernel estimato...
We propose a new bandwidth selection method for kernel estimators of spatial point process intensity...
We propose a new bandwidth selection method for kernel estimators of spatial point process intensity...
We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a...
We investigate the asymptotic mean squared error of kernel estimators of the intensity function of ...
Abstract. Estimation of the intensity function of spatial point processes is a fundamental problem. ...
Point processes describe random point patterns in space. One of their most important characteristics...
Point processes are random local finite sets of points in a space that are used for mod- elling and ...
Dealing with data coming from a space-time inhomogeneous process, there is often the need of semi-pa...
International audienceThis paper is concerned with the problem of estimating covariances of inhomoge...
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
14 pagesLasso and Dantzig selector are standard procedures able to perform variable selection and es...
International audienceThis paper deals with feature selection procedures for spatial point processes...