Detection of Outliers and Patches in Bilinear Time Series Models

We propose a Gibbs sampling algorithm to detect additive outliers and patches of outliers in bilinear time series models based on Bayesian view. We first derive the conditional posterior distributions, and then use the results of first Gibbs run to start the second adaptive Gibbs sampling. It is shown that our procedure could reduce possible effects on masking and swamping. At last, some simulations are performed to demonstrate the efficacy of detection and estimation by Monte Carlo methods.