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Proceedings Paper

Robust estimation of errors-in-variables models using M-estimators
Author(s): Cuiping Guo; Junhuan Peng
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Paper 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.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104204Y (21 July 2017); doi: 10.1117/12.2282452
Show Author Affiliations
Cuiping Guo, China Univ. of Geosciences (China)
Junhuan Peng, China Univ. of Geosciences (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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