Skip to main content
SpringerLink
Log in

Estimation of Radioimmunoassay Data Using Robust Nonlinear Regression Methods

  • Conference paper
Computational Statistics

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H.-P. Altenburg and G. Rosenkranz (1989): Analysis of dose response data. Schriftenreihe Medizinische Statistik, Biomathematik und Inform.-Verarbeitung, Heft 8, Mannheim

    Google Scholar 

  2. R. Dutter and P.J. Huber (1981): Numerical Methods for the Nonlinear Robust Regression Problem. J. Statist. Comput. Simul., 13, 79–113.

    Article  Google Scholar 

  3. P.W. Holland and R.E. Welsch (1977): Robust Regression Using Iteratively Rew. Least-Squares. Commun. Statist.-Theor. Meth., A6, 813–827.

    Google Scholar 

  4. P.J. Huber (1981): Robust Statistics. Wiley, New York.

    Book  Google Scholar 

  5. P. McCullagh (1983): Quasi-likelihood functions. Annals of Statistics, 11, 59–67.

    Article  Google Scholar 

  6. P.J. Rousseeuw and A.M. Leroy (1987): Robust Regression and Outlier Detection. Wiley, New York.

    Book  Google Scholar 

  7. J.J. Tiede and M. Pagano (1979): The Application of Robust Calibration to Radioimmunoassay. Biometrics, 35, 567–574.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fakultät für Klinische Medizin Mannheim, Med. Statistik, Biomathematik und Informationsverarbeitung, University of Heidelberg, Theodor-Kutzer-Ufer, D-6800, Mannheim, Germany

    H.-P. Altenburg

Authors
  1. H.-P. Altenburg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Groupe de Statistique, University of Neuchâtel, Pierre-à-Mazel 7, CH-2000, Neuchâtel, Switzerland

    Yadolah Dodge (Professor of Statistics and Operations Research) (Professor of Statistics and Operations Research)

  2. Lancaster University, GB-Lancaster, LA1 4YE, Great Britain

    Joe Whittaker (Professor of Mathematics) (Professor of Mathematics)

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Altenburg, HP. (1992). Estimation of Radioimmunoassay Data Using Robust Nonlinear Regression Methods. In: Dodge, Y., Whittaker, J. (eds) Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-26811-7_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-26811-7_51

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-26813-1

  • Online ISBN: 978-3-662-26811-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation