Comparison of robust estimators for leveling networks in Monte Carlo simulations
AbstractWe 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.
|Journal series||Reports on Geodesy and Geoinformatics, ISSN 2391-8365, e-ISSN 2391-8152|
|Publication size in sheets||0.55|
|Keywords in English||adjustment; robust estimation; heavy-tailed distribution; internal reliability index|
|project||X. 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
|Score|| = 13.0, 28-11-2017, ArticleFromJournal|
= 13.0, 28-11-2017, ArticleFromJournal
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