Detection of outlier patches in autoregressive time series. (English) Zbl 0978.62081

Summary: This paper proposes a procedure to detect patches of outliers in an autoregressive process. The procedure is an improvement over the existing detection methods via Gibbs sampling. We show that the standard outlier detection via Gibbs sampling may be extremely inefficient in the presence of severe masking and swamping effects. The new procedure identifies the beginning and end of possible outlier patches using the existing Gibbs sampling, then carries out an adaptive procedure with block interpolation to handle patches of outliers. Empirical and simulated examples show that the proposed procedure is effective.


62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)