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Wavelet Based Outlier Correction for Power Controlled Turning Point Detection in Surveillance Systems

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  • Yushu Li

    ()
    (Centre for Labour Market Policy Research (CAFO), Department of Economic and Statistics)

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Abstract

Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.

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File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_29.pdf
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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-29.

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Length: 18
Date of creation: 15 Jul 2011
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Handle: RePEc:aah:create:2011-29

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Unimodel; Turning point; Statistical surveillance; Outlier; Wavelet multiresolution; Threshold.;

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  1. Ling, Shiqing & Li, W.K., 2003. "Asymptotic Inference For Unit Root Processes With Garch(1,1) Errors," Econometric Theory, Cambridge University Press, vol. 19(04), pages 541-564, August.
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