Nonparametric quantile regression with heavy-tailed and strongly dependent errors

被引：4
|
作者：
Honda, Toshio [1 ]
机构：
[1] Hitotsubashi Univ, Grad Sch Econ, Tokyo 1868601, Japan
来源：
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2013年 / 65卷 / 01期
关键词：
Conditional quantile; Random design; Check function; Local linear regression; Stable distribution; Linear process; Long-range dependence; Martingale central limit theorem; TIME-SERIES; EMPIRICAL PROCESSES; DENSITY-ESTIMATION; MOVING AVERAGES; LIMIT-THEOREMS; FUNCTIONALS; SUMS; ASYMPTOTICS; INFERENCE;
D O I：
10.1007/s10463-012-0359-8
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：
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties. It is shown that the asymptotic properties are affected by both the time dependence and the tail index of the errors. The results of a small simulation study are also given.
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页码：23 / 47
页数：25
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