Comparison of robust estimators for leveling networks in Monte Carlo simulations

Maria Pokarowska


We compared the method of least squares (LS), Pope’s iterative data snooping (IDS) and Huber’s M-estimator (HU) in realistic leveling networks, for which the heights or the vertical displacements of points are known. The study was conducted using the Monte Carlo simulation, in which one repeatedly generates sets of observations related to the measurement data, then calculates values of the estimators and, finally, assesses it with respect to the real coordinates. To simulate outliers we used popular mixture models with two or more normal distributions. It is shown that for small, strong networks robust methods IDS and HU are more accurate than LS, but for large, weak networks occurring in practice there is no significant difference between the considered methods in the accuracy of the solution.
Author Maria Pokarowska ZGIPS
Maria Pokarowska,,
- Engineering Geodesy and Control Surveying Systems
Journal seriesReports on Geodesy and Geoinformatics, ISSN 2391-8365, e-ISSN 2391-8152
Issue year2016
Publication size in sheets0.55
Keywords in Englishadjustment; robust estimation; heavy-tailed distribution; internal reliability index
Internal identifier47/2016
projectX. Project leader: Nowak Edward, , Phone: +48 22 234-7751, start date 15-04-2015, planned end date 30-09-2016, 504/01825/1060/40.000XXX, Implemented
WGiK Działalność statutowa
Languageen angielski
2016_Pokarowska_M_Comparison of robust estimators for leveling_47.pdf / 832.52 KB / 2016_Pokarowska_M_Comparison of robust estimators for leveling_47.pdf 832.52 KB
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2016_Oswiadczenie_Pokarowska_M_Comparison_47.pdf (file archived - login or check accessibility on faculty) 2016_Oswiadczenie_Pokarowska_M_Comparison_47.pdf 750.14 KB
Score (nominal)13
ScoreMinisterial score = 13.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) = 13.0, 28-11-2017, ArticleFromJournal
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