The kernel function method developed during the last twenty-five years to estimate a probability density function essentially is a way of smoothing the empirical distribution function. This paper shows how one can generalize this method to estimate counting process intensities using kernel functions to smooth the nonparametric Nelson estimator for the cumulative intensity. The properties of the estimator for the intensity itself are investigated, and uniform consistency and asymptotic normality are proved. We also give an illustrative numerical example.
@article{1176346152,
author = {Ramlau-Hansen, Henrik},
title = {Smoothing Counting Process Intensities by Means of Kernel Functions},
journal = {Ann. Statist.},
volume = {11},
number = {1},
year = {1983},
pages = { 453-466},
language = {en},
url = {http://dml.mathdoc.fr/item/1176346152}
}
Ramlau-Hansen, Henrik. Smoothing Counting Process Intensities by Means of Kernel Functions. Ann. Statist., Tome 11 (1983) no. 1, pp. 453-466. http://gdmltest.u-ga.fr/item/1176346152/