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Using Threshold Accepting to Improve the Computation of Censored Quantile Regression

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COMPSTAT

Abstract

Due to an interpolation property the computation of censored quantile regression estimates corresponds to the solution of a large scale discrete optimization problem. The global optimization heuristic threshold accepting is used in comparison to other algorithms. It can improve the results considerably though it uses more computing time.

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References

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Author information

Authors and Affiliations

  1. Department of Economics, University of Konstanz and University of Mannheim, D-68131, Mannheim, Germany

    Bernd Fitzenberger

  2. Department of Economics, University of Mannheim, D-68131, Mannheim, Germany

    Peter Winker

Authors
  1. Bernd Fitzenberger
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  2. Peter Winker
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Editor information

Editors and Affiliations

  1. Statistics Department, IACR-Rothamsted, AL5 2JQ, Harpenden, Herts, UK

    Roger Payne

  2. Department of Mathematics, University of Bristol, BS8 1TW, Bristol, UK

    Peter Green

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© 1998 Springer-Verlag Berlin Heidelberg

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Fitzenberger, B., Winker, P. (1998). Using Threshold Accepting to Improve the Computation of Censored Quantile Regression. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_40

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  • DOI: https://doi.org/10.1007/978-3-662-01131-7_40

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1131-5

  • Online ISBN: 978-3-662-01131-7

  • eBook Packages: Springer Book Archive

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