Abstract
The assumption of the symmetry of the underlying distribution is important to many statistical inference and modeling procedures. This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information. Based on simulation studies, the new test procedure outperforms other tests of symmetry found in the literature, including the Runs Test of Symmetry. We illustrate our new procedure using real data.
| Original language | American English |
|---|---|
| Journal | Biometrics and Biostatistics International Journal |
| Volume | 3 |
| DOIs | |
| State | Published - Jan 27 2016 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Disciplines
- Biostatistics
- Community Health
- Public Health
Keywords
- Test of symmetry
- Power of the test
- Overlap coefficients
- Kernel Density estimation
- Kullback-Leibler information
- Nonparametric Test
- Kullback-Leibler
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