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An effective selection of regression variables when the error distribution is incorrectly specified

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Summary

An asymptotically efficient selection of regression variables is considered in the situation where the statistician estimates regression parameters by the maximum likelihood method but fails to choose a likelihood function matching the true error distribution. The proposed procedure is useful when a robust regression technique is applied but the data in fact do not require that treatment. Examples and a Monte Carlo study are presented and relationships to other selectors such as Mallows'Cp are investigated.

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Author notes

  1. Wolfgang Härdle

    Present address: Universität Bonn, West Germany

Authors and Affiliations

  1. Johann-Wolfgang-Goethe-Universität, West Germany

    Wolfgang Härdle

  2. University of North Carolina, Carolina, USA

    Wolfgang Härdle

Authors
  1. Wolfgang Härdle
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Additional information

Research supported by Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 123 “Stochastische Mathematische Modelle” and AFOSR Contract No. F49620 82 C 0009.

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Härdle, W. An effective selection of regression variables when the error distribution is incorrectly specified. Ann Inst Stat Math 39, 533–548 (1987). https://doi.org/10.1007/BF02491488

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  • DOI: https://doi.org/10.1007/BF02491488

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