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On a non-parametric confidence interval for the regression slope

Author

Listed:
  • Róbert Tóth

    (Tangent Works)

  • Ján Somorčík

    (Comenius University Bratislava)

Abstract

We investigate an application of the Tukey’s methodology in Theil’s regression to obtain a confidence interval for the true slope in the straight line regression model with not necessarily normal errors. This specific approach is implemented since 2005 in an R package; however, without any theoretical background. We illustrate by Monte Carlo, that this methodology, unlike the classical Theil’s approach, seriously deflates the true confidence level of the resulting interval. We provide also rigorous proofs in case of four (in general) and five data points (under some additional conditions); together with a real life usage example in the latter case. Summing up, we demonstrate that one should never combine statistical methods without checking the assumptions of their usage and we also give a warning to the already wide community of R users of Theil’s regression from various fields of science.

Suggested Citation

  • Róbert Tóth & Ján Somorčík, 2017. "On a non-parametric confidence interval for the regression slope," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 359-369, December.
    • Handle: RePEc:spr:metron:v:75:y:2017:i:3:d:10.1007_s40300-017-0109-z
    DOI: 10.1007/s40300-017-0109-z
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    References listed on IDEAS

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    1. Sapna Kumari & Jeff Nie & Huann-Sheng Chen & Hao Ma & Ron Stewart & Xiang Li & Meng-Zhu Lu & William M Taylor & Hairong Wei, 2012. "Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-17, November.
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