Version 1
: Received: 30 March 2020 / Approved: 31 March 2020 / Online: 31 March 2020 (22:59:14 CEST)
How to cite:
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?. Preprints2020, 2020030467. https://doi.org/10.20944/preprints202003.0467.v1
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?. Preprints 2020, 2020030467. https://doi.org/10.20944/preprints202003.0467.v1
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?. Preprints2020, 2020030467. https://doi.org/10.20944/preprints202003.0467.v1
APA Style
Rofatto, V.F., Matsuoka, M.T., Klein, I., Roberto Veronez, M., & Da Silveira, Jr., L.G. (2020). Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?. Preprints. https://doi.org/10.20944/preprints202003.0467.v1
Chicago/Turabian Style
Rofatto, V.F., Mauricio Roberto Veronez and Luiz Gonzaga Da Silveira, Jr.. 2020 "Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?" Preprints. https://doi.org/10.20944/preprints202003.0467.v1
Abstract
In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.
Keywords
constraints; hypothesis testing; outlier detection; Monte Carlo; quality control; geodesy
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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