ORIGINAL ARTICLE
Proposing a concept of least-squares-based outlier-exposing potential of Gauss-Markov models: Examples in geodesy
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Parzyński Zenon 1, B-C,E
 
 
 
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Faculty of Geodesy and Cartography, Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland
 
 
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
 
 
Submission date: 2024-07-20
 
 
Final revision date: 2024-11-01
 
 
Acceptance date: 2024-11-05
 
 
Publication date: 2024-11-29
 
 
Corresponding author
Prószyński Witold   

Faculty of Geodesy and Cartography, Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland
 
 
Reports on Geodesy and Geoinformatics 2024;118:101-108
 
W.P. theory and analyses, figure design; Z.P. computations, preparation of figures.
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ABSTRACT
Outlier detection and identification are still important issues in the quality control of geodetic networks based on least squares estimation (LSE). In addition to existing network reliability measures, the paper proposes the LSE-based concept (together with the associated measures) of the Outlier-Exposing Potential (OEP) for Gauss-Markov models. The greater the model's redundancy, the more the configuration of its responses to gross errors exposes the location of these errors, and hence, the greater the model's OEP. The potential is given in the basic version and the extended version. The former considers only the effect of the model's redundancy, while the latter also considers the masking effect due to random observation errors at a specified magnitude of gross error. For models that have regions of unidentifiable errors, the corresponding OEP components have zero values. The reflection of OEP in the values of Minimal Identifiable Bias (MIB) is shown. It is proposed that OEP derived based on least squares adjustment be treated as a property of the model itself. The theory is illustrated on several 1D and 2D networks. The research is limited to models with uncorrelated observations and the case of a single gross error. These limitations enabled the formulation of clear properties of general character, not complicated by observation correlations and multiple-outlier combinations.
ACKNOWLEDGEMENTS
The authors would like to express their thanks to Dr. Krzysztof Nowel from the University of Warmia and Mazury in Olsztyn for sharing his original MDB_MIB computation program.
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