Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria
Publication: Journal of Surveying Engineering
Volume 142, Issue 4
Abstract
The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.
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Published In
Journal of Surveying Engineering
Volume 142 • Issue 4 • November 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 28, 2015
Accepted: Feb 16, 2016
Published online: Mar 29, 2016
Discussion open until: Aug 29, 2016
Published in print: Nov 1, 2016
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