25 June 1999 Model selection based on robustness criterion with measurement application
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Abstract
Huber's approach to robust estimation is highly fruitful for solving estimation problems with contaminated data or under incomplete information according to the error structure. A simple selection procedure based on robustness to variations of the errors distribution from the assumed one, is proposed. Minimax M-estimator is used to estimate efficiently the parameters and the measurement quantity. A performance deviation criterion is computed by the mean of the Monte Carlo method improved by the Latin Hypercube Sampling. The selection produced is applied to a real measurement problem, grooves dimensioning using Remote Field Eddy Current inspection.
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Sofiane Brahim-Belhouari, Gilles Fleury, Marie-Eve Davoust, "Model selection based on robustness criterion with measurement application", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351314; https://doi.org/10.1117/12.351314
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