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Linear Programming Techniques in Regression Analysis

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  • E. A. Kiountouzis

Abstract

In this paper simulation techniques are used to evaluate the use of linear programming in regression analysis. The experiments demonstrate that, in certain cases, minimizing the sum of the absolute values of the deviations (L1 norm) is preferable to the Least Squares criterion. No significant bias was found in the L1 norm estimates.

Suggested Citation

  • E. A. Kiountouzis, 1973. "Linear Programming Techniques in Regression Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 69-73, March.
    • Handle: RePEc:bla:jorssc:v:22:y:1973:i:1:p:69-73
    DOI: 10.2307/2346304
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    Cited by:

    1. Wang, Dong Q. & Chukova, Stefanka & Lai, C. D., 2005. "Reducing quadratic programming problem to regression problem: Stepwise algorithm," European Journal of Operational Research, Elsevier, vol. 164(1), pages 79-88, July.
    2. Grigoroudis, Evangelos & Noel, Laurent & Galariotis, Emilios & Zopounidis, Constantin, 2021. "An ordinal regression approach for analyzing consumer preferences in the art market," European Journal of Operational Research, Elsevier, vol. 290(2), pages 718-733.

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