Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
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具有重尾和强相关误差的非参数分位数回归
DOI：
10.1007/s10463-012-0359-8
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发表时间：
2013
影响因子：
1
通讯作者：
Toshio Honda
中科院分区：
文献类型：
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作者：
Fukukawa;H. and Kim;H.;黒田達朗;Tatsuyoshi Saijo;Hiroshi Iyetomi;佐藤仁志;Toshio Honda
我们考虑平稳时间序列条件第 q 分位数的非参数估计。我们在随机设计的设置下处理具有强时间依赖性和重尾的平稳时间序列。我们通过局部线性回归估计条件qth 分位数并研究渐近性质。结果表明，渐近性质受到时间依赖性和误差尾部指数的影响。还给出了小型模拟研究的结果。
We consider nonparametric estimation of the conditionalqth 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 conditionalqth 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.