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On inference for Kendall's τ within a longitudinal data setting

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  • Yan Ma

Abstract

Kendall's τ is a non-parametric measure of correlation based on ranks and is used in a wide range of research disciplines. Although methods are available for making inference about Kendall's τ, none has been extended to modeling multiple Kendall's τs arising in longitudinal data analysis. Compounding this problem is the pervasive issue of missing data in such study designs. In this article, we develop a novel approach to provide inference about Kendall's τ within a longitudinal study setting under both complete and missing data. The proposed approach is illustrated with simulated data and applied to an HIV prevention study.

Suggested Citation

  • Yan Ma, 2012. "On inference for Kendall's τ within a longitudinal data setting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2441-2452, July.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2441-2452
    DOI: 10.1080/02664763.2012.712954
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    References listed on IDEAS

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    1. Marc Hallin & Thomas S. Ferguson & Christian Genest, 2000. "Kendall's tau for serial dependence," ULB Institutional Repository 2013/2093, ULB -- Universite Libre de Bruxelles.
    2. Zhang, Heping & Liu, Ching-Ti & Wang, Xueqin, 2010. "An Association Test for Multiple Traits Based on the Generalized Kendall’s Tau," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 473-481.
    3. G. G. Paulus & F. Grasbon & H. Walther & P. Villoresi & M. Nisoli & S. Stagira & E. Priori & S. De Silvestri, 2001. "Absolute-phase phenomena in photoionization with few-cycle laser pulses," Nature, Nature, vol. 414(6860), pages 182-184, November.
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    Cited by:

    1. 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.

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