In this article, we study a robust estimation method for a general class of integer-valued time series models. The conditional distribution of the process belongs to a broad class of distribution and unlike classical autoregressive framework, the conditional mean of the process also depends on some multivariate exogenous covariate. We derive a robust inference procedure based on the minimum density power divergence. Under certain regularity conditions, we establish that the proposed estimator is consistent and asymptotically normal. Simulation experiments are conducted to illustrate the empirical performances of the estimator. An application to the number of transactions per minute for the stock Ericsson B is also provided.

翻译：本文研究了一类一般整数值时间序列模型的稳健估计方法。该过程的条件分布属于广泛分布类，与经典自回归框架不同，过程的条件均值还依赖于某些多元外生协变量。我们基于最小密度幂散度推导了一种稳健推断流程。在特定正则条件下，我们证明了所提估计量具有相合性和渐近正态性。通过仿真实验展示了该估计量的经验性能，并应用于爱立信B股每分钟交易次数数据。