In this paper, we study a robust estimation method for the observation-driven integer-valued time-series models in which the conditional probability mass of current observations is assumed to follow a negative binomial distribution. Maximum likelihood estimator is highly affected by the outliers. We resort to the minimum density power divergence estimator as a robust estimator and show that it is strongly consistent and asymptotically normal under some regularity conditions. Simulation results are provided to illustrate the performance of the estimator. An application is performed on data for campylobacteriosis infections.

中文翻译：

---

负二项整数值GARCH模型的最小密度幂散估计

在本文中，我们研究了一种基于观测驱动的整数值时间序列模型的鲁棒估计方法，其中假定当前观测的条件概率质量服从负二项式分布。最大似然估计量受异常值的影响很大。我们求助于最小密度功率散度估计器作为鲁棒估计器，并证明它在某些规则性条件下是强一致且渐近正态的。提供仿真结果以说明估计器的性能。对弯曲菌感染的数据进行了应用。