Evaluation of the Robustness of Robust Estimation Methods in Generalized Gaussian Distribution
Abstract In order to choose the most robust estimation methods, observations are simulated by generalized Gaussian distribution (GGD),which can perfectly describe the real distribution of observations. Leveling and trilateration networks with equal weighted and independent observations are taken as examples, and simulation experiments are used to compare the robustness of 12 commonly used robust estimation methods. Our results indicate that when observations obey GGD, the L1, Danish and German-McClure methods, and the IGGⅢ scheme are relatively more efficient robust estimation methods in leveling and trilateration networks with equal weighted and independent observations; this is similar to results in simulation experiments.
Key words :
generalized Gaussian distribution
robust estimation
robustness
leveling network
trilateration network
Fund: Natural Science Foundation of Shanxi Province, No.2012011015-2.
Cite this article:
LIU Jun,XING Pengfei. Evaluation of the Robustness of Robust Estimation Methods in Generalized Gaussian Distribution[J]. jgg, 2016, 36(3): 257-260.
LIU Jun,XING Pengfei. Evaluation of the Robustness of Robust Estimation Methods in Generalized Gaussian Distribution[J]. jgg, 2016, 36(3): 257-260.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2016/V36/I3/257
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