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Nonparametric Test of Symmetry

A collaborative study with Dr. Hani Samawi, Director of the K.E. Peace Center for Biostatistics, and Dr. Robert Vogel, Department Chair of Biostatistics and Epidemiology , provide a more efficient nonparametric test of symmetry based on overlapping coefficients. Parametric and some nonparametric statistical inferences and modeling are valid only under certain assumptions. One of most common assumptions in the literature is that of symmetry of the underlying distribution. If the underlying distribution is not symmetric the question becomes how to define the appropriate location and scale measures. Thus to choose the appropriate statistical analysis, we need to check for underlying assumptions, including symmetry. Most tests of symmetry available in the literature typically have low statistical power and fail to detect a small but meaningful asymmetry in the population. Based on our simulation and real data example, the proposed test of symmetry appears to outperform the other tests of symmetry in the literature in terms of power.

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Posted in Biostatistics, faculty-highlights, JPHCOPH, JPHCOPH, KEPeace CBSR