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OMNE: A new robust membership-set estimator for the parameters of nonlinear models

  • Hélène Lahanier
  • Eric Walter
  • Roberto Gomeni
Article

Abstract

A new method for estimating parameters and their uncertainty is presented. Data are assumed to be corrupted by a noise whose statistical properties are unknown but for which bounds are available at each sampling time. The method estimates the set of all parameter vectors consistent with this hypothesis. Its results are compared with those of the weighted least squares, extended least squares, and biweight robust regression approaches on two data sets, one of which includes 33% outliers. On the basis of these preliminary results, the new method appears to have attractive properties of reliability and robustness.

Key words

Outliers robust estimation uncertainty intervals membership set extended least squares biweight regression weighted least squares 

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Copyright information

© Plenum Publishing Corporation 1987

Authors and Affiliations

  • Hélène Lahanier
    • 1
  • Eric Walter
    • 1
  • Roberto Gomeni
    • 2
  1. 1.Laboratoire des Signaux et SystèmesCNRS Ecole Supérieure d'ElectricitéGif-Sur-YvetteFrance
  2. 2.Centre d'Etudes et de Recherches en Statistiques et Informatique MédicalesSIMEDCréteilFrance

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