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Multiple outlier detection by evaluating redundancy contributions of observations
journal contribution
posted on 2023-05-16, 10:09 authored by Ding, X, Richard ColemanRichard ColemanWhen applying single outlier detection techniques, such as the Tau (Ï„) test, to examine the residuals of observations for outliers, the number of detected observations in any iteration of adjustment is most often more numerous than the actual number of true outliers. A new technique is proposed which estimates the number of outliers in a network by evaluating the redundancy contributions of the detected observations. In this way, a number of potential outliers can be identified and eliminated in each iteration of an adjustment. This leads to higher efficiency in data snooping of geodetic networks. The technique is illustrated with some numerical examples.
History
Publication title
Journal of GeodesyVolume
70Issue
8Pagination
489-498ISSN
0949-7714Department/School
School of Geography, Planning and Spatial SciencesPublisher
Springer-VerlagPlace of publication
Berlin, GermanyRepository Status
- Restricted