Empirical analyses of robustness of the square Msplit estimation

Zbigniew Wiśniewski; Marek Zienkiewicz

doi:10.1515/jag-2020-0009

Empirical analyses of robustness of the square Msplit estimation

Abstract

The paper presents Msplit estimation as an alternative to methods in the class of robust M-estimation. The analysis conducted showed that Msplit estimation is highly efficient in the identification of observations encumbered by gross errors, especially those of small or moderate values. The classical methods of robust estimation provide then unsatisfactory results. Msplit estimation also shows high robustness to single gross errors of large values. The presented analysis of Msplit estimators’ robustness is of a chiefly empirical nature and is based on the example of a simulated levelling network and a real angular-linear network. Using the Monte Carlo method, mean success rates for outlier identification were determined and the courses of empirical influence functions were specified. The outcomes of the analysis were compared with the relevant values achieved via selected methods of robust M-estimation.

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Authors (2)

Zbigniew Wiśniewski Prof.
Marek Zienkiewicz dr inż.

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Category:: Articles
Type:: artykuły w czasopismach
Published in:: Journal of Applied Geodesy no. 15, pages 87 - 104,
ISSN: 1862-9016
Language:: English
Publication year:: 2021
Bibliographic description:: Wiśniewski Z., Zienkiewicz M.: Empirical analyses of robustness of the square Msplit estimation// Journal of Applied Geodesy -Vol. 15,iss. 2 (2021), s.87-104
DOI:: Digital Object Identifier (open in new tab) 10.1515/jag-2020-0009
Verified by:: Gdańsk University of Technology