Estimation of change-point for a class of count time series models.
Estimation of change-point for a class of count time series models.
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DOI：
10.1111/sjos.12489
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发表时间：
2021-12
影响因子：
1
通讯作者：
Zheng, Qi
中科院分区：
文献类型：
--
作者：
Cui, Yunwei;Wu, Rongning;Zheng, Qi
关键词：
我们使用三步序贯方法来估计计数时间序列的变点。在一定的正则性条件下，变点估计按分布收敛于双边随机游动的极大值的位置。基于强混合过程的不变性原理，给出了双边随机游动极大值的闭合近似分布，从而可以对真实变点进行统计推断。这是第一次为整数值时间序列模型提供这样的性质。此外，我们还证明了所提出的方法适用于具有泊松分布或负二项分布的整值自回归条件异方差(INARCH)模型。仿真研究表明，该方法能够很好地定位INARCH模型的变点。并且，通过巴尔的摩市两个街区每周抢劫的经验数据进一步说明了这一过程。
We apply a three-step sequential procedure to estimate the change-point of count time series. Under certain regularity conditions, the estimator of change-point converges in distribution to the location of the maxima of a two-sided random walk. We derive a closed-form approximating distribution for the maxima of the two-sided random walk based on the invariance principle for the strong mixing processes, so that the statistical inference for the true change-point can be carried out. It is for the first time that such properties are provided for integer-valued time series models. Moreover, we show that the proposed procedure is applicable for the integer-valued autoregressive conditional heteroskedastic (INARCH) models with Poisson or negative binomial conditional distribution. In simulation studies, the proposed procedure is shown to perform well in locating the change-point of INARCH models. And, the procedure is further illustrated with empirical data of weekly robbery counts in two neighborhoods of Baltimore City.
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影响因子：
4.5
作者：
DAVIS, RA;HUANG, DW;YAO, YC
通讯作者：
YAO, YC
影响因子：
0.9
作者：
Fokianos, Konstantinos;Fried, Roland
通讯作者：
Fried, Roland
影响因子：
0.8
作者：
Brockwell, A. E.
通讯作者：
Brockwell, A. E.
DOI：
10.1080/03610926.2019.1565843
发表时间：
2020-04-17
影响因子：
0.8
作者：
Cui, Yunwei;Wu, Rongning
通讯作者：
Wu, Rongning
DOI：
10.1080/03610926.2018.1476710
发表时间：
2019-07-03
影响因子：
0.8
作者：
Cui, Yunwei;Wang, Xiaoyin
通讯作者：
Wang, Xiaoyin