In this paper, we propose a computationally valid and theoretically justified methods, the likelihood ratio scan method (LRSM), for estimating multiple change-points in a piecewise stationary generalized conditional integer-valued autoregressive process. LRSM with the usual window parameter $h$ is more satisfied to be used in long-time series with few and even change-points vs. LRSM with the multiple window parameter $h_{mix}$ performs well in short-time series with large and dense change-points. The computational complexity of LRSM can be efficiently performed with order $O((\log n)^3 n)$. Moreover, two bootstrap procedures, namely parametric and block bootstrap, are developed for constructing confidence intervals (CIs) for each of the change-points. Simulation experiments and real data analysis show that the LRSM and bootstrap procedures have excellent performance and are consistent with the theoretical analysis.

翻译：本文提出了一种计算有效且理论合理的方法——似然比扫描法（LRSM），用于估计分段平稳广义条件整数值自回归过程中的多变点。使用常规窗口参数$h$的LRSM更适用于存在稀疏甚至极少变点的长时序数据，而采用多窗口参数$h_{mix}$的LRSM在具有密集且大量变点的短时序数据中表现良好。LRSM的计算复杂度为$O((\log n)^3 n)$，可实现高效执行。此外，我们开发了参数自举和块自举两种自助法，用于构建每个变点的置信区间。模拟实验与真实数据分析表明，LRSM及自助法具有优异性能，且与理论分析结果一致。