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The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form

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  • Oliver R. Cutbill
  • Rami V. Tabri

Abstract

This paper discusses the statistical inference problem associated with testing for dependence between two continuous random variables using Kendall’s Ƭ in the context of the missing data problem. We prove the worst-case identified set for this measure of association always includes zero. The consequence of this result is that robust inference for dependence using Kendall’s Ƭ, where robustness is with respect to the form of the missingness-generating process, is impossible.

Suggested Citation

  • Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
    • Handle: RePEc:syd:wpaper:2022-03
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    References listed on IDEAS

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    Keywords

    Impossible Inference; Statistical Dependence; Kendall’s Ƭ; Partial Identification; Missing Data;
    All these keywords.

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