Many statistical applications and inferences rely on the validity of the underlying distributional assumption. Symmetry of the underlying distribution is essential in many statistical inference and modeling procedures.
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 base deficit data.
“A Test of Symmetry Based on the Kernel KullbackLeibler Information with Application to Base Deficit Data,” was published in Biometrics & Biostatistics International Journal.
Dr. Hani Samawi, director of the K.E. Peace Center for Biostatistics at Georgia Southern University Jiann-Ping Hsu College of Public Health was the lead author and Dr. Robert Vogel, dual department chair for biostatistics and epidemiology was the co-author.