Adaptive estimation of the conditional intensity of marker-dependent counting processes
Published in Annales de l’Institut Henri Poincaré - Probabilités et Statistiques, 2010
F. Comte, S. Gaïffas and A. Guilloux
We propose in this work an original estimator of the conditional intensity of a marker-dependent counting process, that is, a counting process with covariates. We use model selection methods and provide a nonasymptotic bound for the risk of our estimator on a compact set. We show that our estimator reaches automatically a convergence rate over a functional class with a given (unknown) anisotropic regularity. Then, we prove a lower bound which establishes that this rate is optimal. Lastly, we provide a short illustration of the way the estimator works in the context of conditional hazard estimation.
