New strategies for the detection of influential observations

Marc Hofmann, Cristian Gatu and Erricos John Kontoghioghes
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Marc Hofmann: University of Neuchatel
Cristian Gatu: University of Neuchatel
Erricos John Kontoghioghes: University of Cyprus
Authors registered in the RePEc Author Service: Erricos John Kontoghiorghes

No 409, Computing in Economics and Finance 2006 from Society for Computational Economics

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.

Date: 2006-07-04
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:409

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