Uniformly root-$N$ consistent density estimators for weakly dependent invertible linear processes
Statistics Theory
2009-09-29 v1 Statistics Theory
Abstract
Convergence rates of kernel density estimators for stationary time series are well studied. For invertible linear processes, we construct a new density estimator that converges, in the supremum norm, at the better, parametric, rate . Our estimator is a convolution of two different residual-based kernel estimators. We obtain in particular convergence rates for such residual-based kernel estimators; these results are of independent interest.
Cite
@article{arxiv.0708.1913,
title = {Uniformly root-$N$ consistent density estimators for weakly dependent invertible linear processes},
author = {Anton Schick and Wolfgang Wefelmeyer},
journal= {arXiv preprint arXiv:0708.1913},
year = {2009}
}
Comments
Published at http://dx.doi.org/10.1214/009053606000001352 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)