Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes
Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes
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DOI：
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发表时间：
2006
影响因子：
1.4
通讯作者：
Songxi Chen;C. M. Wong
中科院分区：
文献类型：
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作者：
Songxi Chen;C. M. Wong
与相依观测相关联的分位数推断是风险管理中的一项常见任务。本文利用经验似然法构造弱相依过程平稳分布分位数的置信区间。为了适应数据相关性并避免任何二次方差估计，经验似然基于观测块来制定。为了减少置信区间的长度，加权经验分布通过核函数进行平滑。这表明，重新缩放版本的平滑块经验似然比承认一个有限的卡方分布与一个自由度，并有利于似然比置信区间的分位数。这些置信区间的实际性能进行了评估，在模拟研究。
Inference on quantiles associated with dependent observation is a com- mon task in risk management. This paper employs empirical likelihood to construct confidence intervals for quantiles of the stationary distribution of a weakly depen- dent process. To accommodate data dependence and avoid any secondary variance estimation, empirical likelihood is formulated based on blocks of observations. To reduce the length of the confidence intervals, the weighted empirical distribution is smoothed by a kernel function. This shows that a rescaled version of the smoothed block empirical likelihood ratio admits a limiting chi-square distribution with one degree of freedom and facilitates likelihood ratio confidence intervals for quantiles. The practical performance of these confidence intervals is evaluated in a simulation study.