论文标题
Poisson QMLE用于更改点检测的一般整数值时间序列模型
Poisson QMLE for change-point detection in general integer-valued time series models
论文作者
论文摘要
我们一起考虑了整个数字值时间序列中的回顾性和顺序更改点检测。 该过程的条件均值取决于参数$θ^*$，该参数可能会随着时间而变化。我们提出了基于参数的Poisson准最大似然估计器以及计算更新的估计器的过程，而在没有顺序框架中没有历史观察的情况下。对于回顾性和顺序检测，测试统计量会收敛于在替代方案下无变化和差异到无穷大的零假设下从标准布朗运动获得的一些分布。也就是说，这些程序是一致的。 提供了一些模拟的结果以及实际数据应用程序。
We consider together the retrospective and the sequential change-point detection in a general class of integer-valued time series. The conditional mean of the process depends on a parameter $θ^*$ which may change over time. We propose procedures which are based on the Poisson quasi-maximum likelihood estimator of the parameter, and where the updated estimator is computed without the historical observations in the sequential framework. For both the retrospective and the sequential detection, the test statistics converge to some distributions obtained from the standard Brownian motion under the null hypothesis of no change and diverge to infinity under the alternative; that is, these procedures are consistent. Some results of simulations as well as real data application are provided.
