scholarly journals A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions

Biometrika ◽  
2018 ◽  
Vol 105 (2) ◽  
pp. 455-462 ◽  
Author(s):  
O Cronie ◽  
M N M Van Lieshout
Keyword(s):  
Bandwidth Selection ◽  
Kernel Estimators ◽  
Model Based ◽  
Spatial Intensity ◽  
Selection For ◽  
Intensity Functions

scholarly journals Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions

2019 ◽  
Vol 22 (3) ◽  
pp. 995-1008
Author(s):  
M. N. M. van Lieshout
Keyword(s):  
Point Process ◽  
Mean Squared Error ◽  
Bandwidth Selection ◽  
Intensity Function ◽  
Kernel Estimators ◽  
Infill Asymptotics ◽  
Squared Error ◽  
Selection For ◽  
Asymptotic Mean Squared Error ◽  
Intensity Functions

AbstractWe investigate the asymptotic mean squared error of kernel estimators of the intensity function of a spatial point process. We derive expansions for the bias and variance in the scenario that n independent copies of a point process in $\mathbb {R}^{d}$ ℝ d are superposed. When the same bandwidth is used in all d dimensions, we show that an optimal bandwidth exists and is of the order n− 1/(d+ 4) under appropriate smoothness conditions on the true intensity function.

scholarly journals Optimal bandwidth selection for recursive Gumbel kernel density estimators

2019 ◽  
Vol 7 (1) ◽  
pp. 375-393
Author(s):  
Yousri Slaoui
Keyword(s):  
Probability Density Function ◽  
Strong Consistency ◽  
Bandwidth Selection ◽  
Kernel Density ◽  
Kernel Estimator ◽  
Data Driven ◽  
Kernel Estimators ◽  
Convergence Properties ◽  
Selection For ◽  
Selection Of

AbstractIn this paper, we propose a data driven bandwidth selection of the recursive Gumbel kernel estimators of a probability density function based on a stochastic approximation algorithm. The choice of the bandwidth selection approaches is investigated by a second generation plug-in method. Convergence properties of the proposed recursive Gumbel kernel estimators are established. The uniform strong consistency of the proposed recursive Gumbel kernel estimators is derived. The new recursive Gumbel kernel estimators are compared to the non-recursive Gumbel kernel estimator and the performance of the two estimators are illustrated via simulations as well as a real application.

On Adaptive Bandwidth Selection for Efficient MIA

2014 ◽  
pp. 82-97 ◽  
Author(s):  
Mathieu Carbone ◽  
Sébastien Tiran ◽  
Sébastien Ordas ◽  
Michel Agoyan ◽  
Yannick Teglia ◽  
...  
Keyword(s):  
Bandwidth Selection ◽  
Selection For ◽  
Adaptive Bandwidth ◽  
Adaptive Bandwidth Selection
Sign in / Sign up

Export Citation Format

Share Document