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

Detection of additive outliers in Poisson INteger-valued AutoRegressive time series