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Permutation-based multivariate regression analysis: The case for least sum of absolute deviations regression

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  • Paul Mielke
  • Kenneth Berry

Abstract

Linear and nonlinear multivariate least sum of absolute deviations regression models are profiled and evaluated. A chance-corrected measure of agreement between observed and predicted values is presented, a technique for establishing empirically-derived quantile limits for predicted values is introduced, and a permutation-based inference procedure for the measure of agreement is described. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Paul Mielke & Kenneth Berry, 1997. "Permutation-based multivariate regression analysis: The case for least sum of absolute deviations regression," Annals of Operations Research, Springer, vol. 74(0), pages 259-268, November.
    • Handle: RePEc:spr:annopr:v:74:y:1997:i:0:p:259-268:10.1023/a:1018926522359
    DOI: 10.1023/A:1018926522359
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    Cited by:

    1. Mike G. Tsionas, 2021. "Multi-criteria optimization in regression," Annals of Operations Research, Springer, vol. 306(1), pages 7-25, November.
    2. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.

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