A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions
From MaRDI portal
Publication:5384597
DOI10.1093/biomet/asy001OpenAlexW2793282548MaRDI QIDQ5384597
Ottmar Cronie, Marie-Colette N. M. van Lieshout
Publication date: 24 June 2019
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://ir.cwi.nl/pub/27709
Related Items (15)
Semiparametric Multinomial Logistic Regression for Multivariate Point Pattern Data ⋮ BAYESIAN SELECTION OF LOCAL BANDWIDTH IN NON-HOMOGENEOUS POISSON PROCESS KERNEL ESTIMATORS FOR THE INTENSITY FUNCTION ⋮ Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data ⋮ Infill asymptotics for adaptive kernel estimators of spatial intensity ⋮ Parametric estimation of spatial-temporal point processes using the Stoyan-Grabarnik statistic ⋮ Bayesian Selection of Adaptive Bandwidth in Non-homogeneous Poisson Process Kernel Estimators for the Intensity Function ⋮ Shrinkage estimators of the spatial relative risk function ⋮ Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions ⋮ Inhomogeneous higher-order summary statistics for point processes on linear networks ⋮ Infill asymptotics and bandwidth selection for kernel estimators of spatial intensity functions ⋮ Globally intensity-reweighted estimators for $K$- and pair correlation functions ⋮ Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates ⋮ Diffusion smoothing for spatial point patterns ⋮ Resample-smoothing of Voronoi intensity estimators ⋮ Global Scan Methods for Comparing Two Spatial Point Processes
This page was built for publication: A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions
