We study the problem of intervention effects generating various types of outliers in an integer-valued autoregressive model with Poisson innovations. We concentrate on outliers which enter the dynamics and can be seen as effects of extraordinary events. We consider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect of unknown type at a known time and the detection of an intervention effect when both the type and the time are unknown. We develop F-tests and score tests for the first scenario. For the second and third scenarios we rely on the maximum of the different F-type or score statistics. The usefulness of the proposed approach is illustrated using monthly data on human brucellosis infections in Greece.

翻译：我们研究了在泊松创新下的整数值自回归模型中，干预效应生成各类异常值的问题。重点聚焦于那些进入动态过程、可视为异常事件影响的异常值。我们考虑三种不同情形：即已知类型且已知时间的干预效应检测、已知时间但类型未知的干预效应检测，以及类型与时间均未知的干预效应检测。针对第一种情形，我们开发了F检验和得分检验；对于第二和第三种情形，则依赖于不同F型或得分型统计量的最大值。利用希腊人类布鲁氏菌病感染的月度数据，验证了所提出方法的实用性。