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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||04 Jul 2006|
|Date of revision:|
|Contact details of provider:|| Web page: http://comp-econ.org/|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:409. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.