Skip to main content
SpringerLink
Log in

Computational Aspects in Maximum Penalized Likelihood Estimation

  • Conference paper
Statistical Modelling

Part of the book series: Lecture Notes in Statistics ((LNS,volume 104))

  • 489 Accesses

Abstract

In this paper we describe the technical details for implementing maximum penalized likelihood estimation (MPLE). This includes description of software for fitting weighted cubic smoothing splines, which constitute building blocks in MPLE. An example is given for illustration.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Chambers, J.M. and Hastie, T.J. (1993) Statistical models in S. Chapman and Hall, New York.

    Google Scholar 

  • Cole, T.J. and Green, P.J. (1992). Smoothing reference centile curves: the LMS method and penalized likelihood. Statistics in Medicine, 11, 1305–1319.

    Google Scholar 

  • de Boor, C. (1978). A practical guide to splines. Springer-Verlag, New York.

    Book  MATH  Google Scholar 

  • Eubank, R.L. (1988). Spline smoothing and nonparametric regression. Dekker, New York.

    MATH  Google Scholar 

  • Green, P.J. and Silverman, B.W. (1994). Nonparametric regression and generalized linear models: a roughness penalty approach. Chapman and Hall, London.

    Google Scholar 

  • Härdle, W. (1991). Applied nonparametric regression. Cambridge university press, Cambridge.

    MATH  Google Scholar 

  • Hastie, T.J. and Tibshirani, R.J. (1990). Generalized additive models. Chapman and Hall, London.

    MATH  Google Scholar 

  • Reinsch, C. (1967). Smoothing by spline functions. Numerische Mathematik, 10, 177–183.

    Article  MathSciNet  MATH  Google Scholar 

  • Rosen, O. and Cohen, A. (1995). Extreme percentile regression. In COMPSTAT,proceedings in computational statistics,eds W. Härdle and M.G. Schimek, Vienna: Physica-Verlag. To appear.

    Google Scholar 

Download references

Authors
  1. Ori Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayala Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Statistik, Universität Innsbruck, Christoph-Probst-Platz, Innsbruck, A-6020, Austria

    Gilg U. H. Seeber  & Gabriele Steckel-Berger  & 

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

    Brian J. Francis

  3. Institut für Statistik, Wirtschaftsuniversität, Augasse 2-6, Wien, A-1090, Austria

    Reinhold Hatzinger

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media New York

About this paper

Cite this paper

Rosen, O., Cohen, A. (1995). Computational Aspects in Maximum Penalized Likelihood Estimation. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-0789-4_32

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94565-1

  • Online ISBN: 978-1-4612-0789-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation