Title: Bootstrapping kernel intensity estimation for nonhomogeneous point processes depending on spatial covariates,
Abstract: In the spatial point process context, kernel intensity estimation has been
mainly restricted to exploratory analysis due to its lack of consistency.
Different methods have been analysed to overcome this problem, and the
inclusion of covariates resulted to be one possible solution. In this paper we
focus on de\-fi\-ning a theoretical framework to derive a consistent kernel
intensity estimator using covariates, as well as a consistent smooth bootstrap
procedure. We define two new data-driven bandwidth selectors specifically
designed for our estimator: a rule-of-thumb and a plug-in bandwidth based on
our consistent bootstrap method. A simulation study is accomplished to
understand the performance of our proposals in finite samples. Finally, we
describe an application to a real data set consisting of the wildfires in
Canada during June 2015, using meteorological information as covariates.