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Comparison of robust estimators for leveling networks in Monte Carlo simulations / Maria Pokarowska in Reports on geodesy and geoinformatics, vol 101 (June 2016)  

Public [article]  inReports on geodesy and geoinformatics > vol 101 (June 2016) . - pp 70 - 81 Titre : Comparison of robust estimators for leveling networks in Monte Carlo simulations Type de document : Article/Communication Auteurs : Maria Pokarowska, Auteur Année de publication : 2016 Article en page(s) : pp 70 - 81 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Nivellement [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode des moindres carrés [Termes IGN] méthode robuste [Termes IGN] réseau de nivellement [Termes IGN] valeur aberrante Résumé : (Auteur) 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. Numéro de notice : A2016-653 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/rgg-2016-0023 En ligne : http://dx.doi.org/10.1515/rgg-2016-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81872 [article]