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Estimation of Radioimmunoassay Data Using Robust Nonlinear Regression Methods

  • H.-P. Altenburg

Abstract

The paper discusses several nonlinear regression methods estimating contaminated radioimmunoassay data. The underlying model is an overdispersed Poisson process with four regression line parameters and one parameter related to the overdispersion of the variance. A generalized least-squares (GLS) algorithm can be used for parameter estimation of noncontaminated data. In the presence of outliers different methods are discussed such as L p -norm or nonlinear generalizations of Huber’s M-estimator. The best estimation results we get by a winsorized version of the GLS algorithm.

Keywords

Ordinary Little Square Generalize Little Square Scale Estimate Result Parameter Estimate Good Estimation Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • H.-P. Altenburg
    • 1
  1. 1.Fakultät für Klinische Medizin Mannheim, Med. Statistik, Biomathematik und InformationsverarbeitungUniversity of HeidelbergMannheimGermany

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