  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.
