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Bayesian Semiparametric Seemingly Unrelated Regression

  • Conference paper
Compstat

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Abstract

Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated.

A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covariates. In this paper, we develop a semiparametric SUR model based on Bayesian P-splines. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques.

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References

  • de Boor (1978): A Practical Guide to Splines. Springer-Verlag, New York.

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  • Lang, S. and Brezger, A. (2001): Bayesian P-Splines. SFB 386 discussion paper 236, University of Munich.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Munich, Ludwigstr. 33, Munich, 80539, Germany

    Stefan Lang, Samson B. Adebayo & Ludwig Fahrmeir

Authors
  1. Stefan Lang
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  2. Samson B. Adebayo
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  3. Ludwig Fahrmeir
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Editor information

Editors and Affiliations

  1. CASE — Centre of Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Straße 1, 10178, Berlin, Germany

    Wolfgang Härdle

  2. Wirtschaftswissenschaftliche Fakultät, Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, Spandauer Straße 1, 10178, Berlin, Germany

    Bernd Rönz

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© 2002 Springer-Verlag Berlin Heidelberg

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Lang, S., Adebayo, S.B., Fahrmeir, L. (2002). Bayesian Semiparametric Seemingly Unrelated Regression. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_25

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  • DOI: https://doi.org/10.1007/978-3-642-57489-4_25

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1517-7

  • Online ISBN: 978-3-642-57489-4

  • eBook Packages: Springer Book Archive

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