New strategies for the detection of influential observations
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.
Year of publication: |
2006-07-04
|
---|---|
Authors: | Hofmann, Marc ; Gatu, Cristian ; Kontoghioghes, Erricos John |
Institutions: | Society for Computational Economics - SCE |
Saved in:
Saved in favorites
Similar items by person
-
A graph approach to generate all possible subset regression models
Gatu, Cristian, (2006)
-
A branch and bound algorithm for computing the best subset regression models
Gatu, Cristian, (2002)
-
Efficient algorithms for computing the best subset regression models for large-scale problems
Hofmann, Marc, (2007)
- More ...