  Outlying observations are commonly encountered in the analysis of time
series. In this paper the problem of detecting additive outliers in
integer-valued time series is considered. We show how Gibbs sampling can be
used to detect outlying observations in INAR(1) processes. The methodology
proposed is illustrated using examples as well as an observed data set.
