Detection of Outliers and Patches in Bilinear Time Series Models | Zendy

Ping Chen | Zendy; Li Ling | Zendy; Ye Liu | Zendy; JinGuan Lin | Zendy

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Detection of Outliers and Patches in Bilinear Time Series Models

Author(s) -

Ping Chen,

Li Ling,

Ye Liu,

JinGuan Lin

Publication year - 2010

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2010/580583

Subject(s) - gibbs sampling , outlier , bilinear interpolation , series (stratigraphy) , bayesian probability , monte carlo method , sampling (signal processing) , markov chain monte carlo , bayesian inference , computer science , metropolis–hastings algorithm , time series , algorithm , statistics , mathematics , pattern recognition (psychology) , artificial intelligence , detector , paleontology , telecommunications , biology

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

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