21 July 2017 Robust estimation of errors-in-variables models using M-estimators
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104204Y (2017) https://doi.org/10.1117/12.2282452
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
The traditional Errors-in-variables (EIV) models are widely adopted in applied sciences. The EIV model estimators, however, can be highly biased by gross error. This paper focuses on robust estimation in EIV models. A new class of robust estimators, called robust weighted total least squared estimators (RWTLS), is introduced. Robust estimators of the parameters of the EIV models are derived from M-estimators and Lagrange multiplier method. A simulated example is carried out to demonstrate the performance of the presented RWTLS. The result shows that the RWTLS algorithm can indeed resist gross error to achieve a reliable solution.
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Cuiping Guo, Junhuan Peng, "Robust estimation of errors-in-variables models using M-estimators", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104204Y (21 July 2017); doi: 10.1117/12.2282452; https://doi.org/10.1117/12.2282452
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