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Stepwise local influence analysis

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  • Shi, Lei
  • Huang, Mei
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    Abstract

    A new method called stepwise local influence analysis is proposed to detect influential observations and to identify masking effects in a dataset. Influential observations are detected step-by-step such that any highly influential observations identified in a previous step are removed from the perturbation in the next step. The process iterates until no further influential observations can be found. It is shown that this new method is very effective to identify the influential observations and has the power to uncover the masking effects. Additionally, the issues of constraints on perturbation vectors and bench-mark determination are discussed. Several examples with regression models and linear mixed models are illustrated for the proposed methodology.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-50S8PFB-1/2/f252ffb7d2d9b16c141ba3715e98160b
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 2 (February)
    Pages: 973-982

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:2:p:973-982

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Local influence analysis Influential observations Subset perturbation scheme Masking effects;

    References

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    1. Shi, Lei & Ojeda, Mario Miguel, 2004. "Local influence in multilevel regression for growth curves," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 282-304, November.
    2. W.-Y. Poon & Y. S. Poon, 1999. "Conformal normal curvature and assessment of local influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 51-61.
    3. Frank Critchley & Richard A. Atkinson & Guobing Lu & Elenice Biazi, 2001. "Influence analysis based on the case sensitivity function," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 307-323.
    4. Shi, Lei & Chen, Gemai, 2008. "Case deletion diagnostics in multilevel models," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 1860-1877, October.
    5. Hong-Tu Zhu & Sik-Yum Lee, 2001. "Local influence for incomplete data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 111-126.
    6. Frank Critchley, 2004. "Data-informed influence analysis," Biometrika, Biometrika Trust, vol. 91(1), pages 125-140, March.
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    Cited by:
    1. Schützenmeister, André & Piepho, Hans-Peter, 2012. "Residual analysis of linear mixed models using a simulation approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1405-1416.

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