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A nonparametric test of symmetry based on the overlapping coefficient

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  • Hani M. Samawi
  • Amal Helu
  • Robert Vogel

Abstract

In this paper, we introduce a new nonparametric test of symmetry based on the empirical overlap coefficient using kernel density estimation. Our investigation reveals that the new test is more powerful than the runs test of symmetry proposed by McWilliams [31]. Intensive simulation is conducted to examine the power of the proposed test. Data from a level I Trauma center are used to illustrate the procedures developed in this paper.

Suggested Citation

  • Hani M. Samawi & Amal Helu & Robert Vogel, 2011. "A nonparametric test of symmetry based on the overlapping coefficient," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 885-898, February.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:885-898
    DOI: 10.1080/02664761003692365
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    References listed on IDEAS

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    1. Gordon Anderson & Oliver Linton & Yoon-Jae Wang, 2009. "Non Parametric Estimation of a Polarization Measure," Working Papers tecipa-363, University of Toronto, Department of Economics.
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    Cited by:

    1. Eidous, Omar M. & Ananbeh, Enas A., 2024. "Kernel method for estimating overlapping coefficient using numerical integration methods," Applied Mathematics and Computation, Elsevier, vol. 462(C).

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