Skip to main content
SpringerLink
Log in
Book cover

Advances in GLIM and Statistical Modelling pp 20–26Cite as

Residuals and Influential Points in Mean Structures Estimated with Pseudo Maximum Likelihood Methods

  • Conference paper

Part of the book series: Lecture Notes in Statistics ((LNS,volume 78))

Abstract

Pseudo maximum likelihood (PML) has been developed by Gourieroux, Monfort and Trognon (1984) for the estimation of mean structures when the distribution function of the error term is not known. Regularity conditions and technical proofs are found in their paper. Their results are briefly reviewed and related to quasi ML estimation of generalized linear models (GLM’s) (McCullagh and Nelder 1989). Then residuals and influential points are discussed.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Cook, R.D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman and Hall.

    MATH  Google Scholar 

  • Gourieroux, C., Monfort, A. and Trognon, A. (1984), “Pseudo Maximum Likelihood Methods: Theory”, Econometrica, 52, 681–700.

    Article  MathSciNet  MATH  Google Scholar 

  • Kale, B.K. (1962), “On the Solution of the Likelihood Equation by Iteration Processes — The Multiparametric Case”, Biometrika, 48, 452–456.

    MathSciNet  Google Scholar 

  • Kennedy, W.J. and Gentle, J. E. (1980), Statistical Computing, New York and Basel: Marcel Dekker.

    MATH  Google Scholar 

  • McCullagh, P. and Nelder, J.A. (1989), Generalized Linear Models, 2nd edn., New York: Chapman and Hall.

    MATH  Google Scholar 

  • Pregibon, D. (1981), “Logistic Regression Diagnostics”, Annals of Statistics, 9, 705–724.

    Article  MathSciNet  MATH  Google Scholar 

  • Wedderburn, R.W.M. (1974), “Quasi-Likelihood-Functions, Generalized Linear Models and the Gauss-Newton-Method”, Biometrika, 61, 439–447.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, Bergische Universität Wuppertal, Gaußstr. 20, D-5600, Wuppertal, Germany

    Gerhard Arminger

Authors
  1. Gerhard Arminger
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Seminar für Statistik, University of Munich, D-8000, Munich 22, Germany

    Ludwig Fahrmeir

  2. Centre for Applied Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF, UK

    Brian Francis

  3. Faculty of Science, Computing and Engineering, Polytechnic of North London, London, N7 8DB, England

    Robert Gilchrist

  4. Institut für Statistik, University of Regensburg, Germany

    Gerhard Tutz

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag New York, Inc.

About this paper

Cite this paper

Arminger, G. (1992). Residuals and Influential Points in Mean Structures Estimated with Pseudo Maximum Likelihood Methods. In: Fahrmeir, L., Francis, B., Gilchrist, R., Tutz, G. (eds) Advances in GLIM and Statistical Modelling. Lecture Notes in Statistics, vol 78. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2952-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-2952-0_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97873-4

  • Online ISBN: 978-1-4612-2952-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation