We consider kernel estimation of marginal densities and regression functions of stationary processes. It is shown that for a wide class of time series, with proper centering and scaling, the maximum deviations of kernel density and regression estimates are asymptotically Gumbel. Our results substantially generalize earlier ones which were obtained under independence or beta mixing assumptions. The asymptotic results can be applied to assess patterns of marginal densities or regression functions via the construction of simultaneous confidence bands for which one can perform goodness-of-fit tests. As an application, we construct simultaneous confidence bands for drift and volatility functions in a dynamic short-term rate model for the U.S. Treasury yield curve rates data.
Publié le : 2010-08-15
Classification:
Gumbel distribution,
kernel density estimation,
linear process,
maximum deviation,
nonlinear time series,
nonparametric regression,
simultaneous confidence band,
stationary process,
treasury bill data,
62H15,
62G10
@article{1278861252,
author = {Liu, Weidong and Wu, Wei Biao},
title = {Simultaneous nonparametric inference of time series},
journal = {Ann. Statist.},
volume = {38},
number = {1},
year = {2010},
pages = { 2388-2421},
language = {en},
url = {http://dml.mathdoc.fr/item/1278861252}
}
Liu, Weidong; Wu, Wei Biao. Simultaneous nonparametric inference of time series. Ann. Statist., Tome 38 (2010) no. 1, pp. 2388-2421. http://gdmltest.u-ga.fr/item/1278861252/