Abstract
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.
Highlights
Dynamic systems or engineering time series observations 1 are often perturbed by some interrupting phenomena which generate aberrant data
We propose a Gibbs sampling algorithm to detect additive outliers and patches of outliers in bilinear time series models based on Bayesian view
This paper considers detection of outliers and patches in bilinear time series models, which are one of the fractal time series models 9
Summary
Dynamic systems or engineering time series observations 1 are often perturbed by some interrupting phenomena which generate aberrant data They may be due to some unusual events, such as sudden disturbed factor, noise, and even recording errors. Some authors have considered the detection problem in the linear and nonlinear models. Based on some different prior distributions, a new Gibbs sampling algorithm is proposed for identifying additive isolated outliers and patches of outliers in bilinear time series. This paper considers detection of outliers and patches in bilinear time series models, which are one of the fractal time series models 9.
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