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New strategies for the detection of influential observations

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Author Info

  • Marc Hofmann

    (University of Neuchatel)

  • Cristian Gatu

    (University of Neuchatel)

  • Erricos John Kontoghioghes

    (University of Cyprus)

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    Abstract

    Efficient algorithms for diagnosing influential data points are investigated. Techniques examining potentially influential subsets are considered. Given a list of candidate observations, a new row-dropping algorithm (RDA) computes all possible observation-subset regression models. It employs a Cholesky updating algorithm using Givens rotations. The algorithm is organized via the all-subsets tree. The number of cases needed to be considered by multiple-row methods rapidly exhausts available computing power. The tree's structure is exploited to effect a parallel algorithm. Strategies using statistical information to prune the tree and narrow the search space are investigated.

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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 409.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:409

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