Publications Repository - Gdańsk University of Technology

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.

Authors

• Prof. Zbigniew Wiśniewski,
• dr inż. Marek Zienkiewicz link open in new tab