Quantile Curve Estimation and Visualization for Nonstationary Time Series

被引:27
作者
Draghicescu, Dana [1 ]
Guillas, Serge [2 ]
Wu, Wei Biao [3 ]
机构
[1] CUNY Hunter Coll, Dept Math & Stat, New York, NY 10065 USA
[2] UCL, Dept Stat Sci, London WC1E 6BT, England
[3] Univ Chicago, Dept Stat, Chicago, IL 60637 USA
关键词
Nonstationary time series; Ozone; Quantile estimation; Quantile visualization; Smoothing; REGRESSION QUANTILES; KERNEL;
D O I
10.1198/jcgs.2009.0001
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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 article we address the problem of quantile curve estimation for a wide class of nonstationary and/or non-Gaussian processes. We discuss several nonparametric quantile curve estimates, give asymptotic results, and propose a data-driven procedure for the selection of smoothing parameters. This methodology provides a statistically reliable and computationally efficient graphical tool that can be used for the exploration and visualization of the behavior of time-varying quantiles for nonstationary time series. A Monte Carlo simulation study and two applications to ozone time series illustrate our method. R codes with the algorithm for selection of smoothing parameters (described in Section 3) are available in the online supplements.
引用
收藏
页码:1 / 20
页数:20
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