|
Organizers |
Estimating time-varying quantiles of nearly stationary stochastic processes, with applications to ozone time series
by
Serge Guillas
Georgia Institute of Technology
Coauthors: Dana Draghicescu (CUNY), Wei Biao Wu (U. of Chicago)
There is an increasing interest in studying time-varying quantiles, particularly for environmental processes. For instance, high pollution levels may cause severe respiratory problems, and large precipitation amounts can damage the environment and have negative impacts on the society. In this paper we address the problem of quantile estimation for a wide class of stochastic processes, allowing for nonstationarity, nongaussianity and nondiffirentiability of the quantile function. We propose a two-step nonparametric quantile estimation procedure. We cut up the sample path of the process into different blocks of constant length and select the optimal length of the blocks by minimizing a penalized mean squared error of the initial estimator. Kernel smoothing is then used to improve the estimation of the quantile curve. Asymptotic properties are analyzed in a general setting. Small sample properties are examined through simulation studies. Applications to stratospheric and ground-level ozone time series illustrate the findings.
Date received: October 11, 2005
Copyright © 2005 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # carm-45.