Covariances Estimation for Long-Memory Processes
Covariances Estimation for Long-Memory Processes
复制标题
长记忆过程的协方差估计
DOI：
10.1239/aap/1269611147
复制
发表时间：
2010
影响因子：
1.2
通讯作者：
W. Zheng
中科院分区：
文献类型：
--
作者：
W. Wu;Yinxiao Huang;W. Zheng
对于时间序列，样本协方差图是评估其相关性的一种流行方法。本文系统地刻画了长记忆线性过程样本协方差的渐近性质。得到了样本协方差有界滞后和无界滞后的中心极限定理和非中心极限定理。结果表明，极限分布以一种非常有趣的方式依赖于依赖的强度、创新的重尾性和滞后的大小。
For a time series, a plot of sample covariances is a popular way to assess its dependence properties. In this paper we give a systematic characterization of the asymptotic behavior of sample covariances of long-memory linear processes. Central and noncentral limit theorems are obtained for sample covariances with bounded as well as unbounded lags. It is shown that the limiting distribution depends in a very interesting way on the strength of dependence, the heavy-tailedness of the innovations, and the magnitude of the lags.