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Robust Transformations in Univariate and Multivariate Time Series

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  • Marco Riani
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    Abstract

    It is well known that transformation of the response may improve the homogeneity and the approximate normality of the errors. Unfortunately, the estimated transformation and related test statistic may be sensitive to the presence of one, or several, atypical observations. In addition, it is important to remark that outliers in one transformed scale may not be atypical in another scale. Therefore, it is important to choose a transformation which does not depend on the presence of particular observations. In this article we suggest an efficient procedure based on a robust score test statistic which quantifies the effect of each observation on the choice of the transformation.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930802388074
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

    Volume (Year): 28 (2009)
    Issue (Month): 1-3 ()
    Pages: 262-278

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    Handle: RePEc:taf:emetrv:v:28:y:2009:i:1-3:p:262-278

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    Related research

    Keywords: Fan plot; Forward search; Kalman filter; Outlier detection; Robust methods; Score test;

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