An Optimal Nonparametric Test of Symmetry Based on the Overlapping Coefficient Using an Extreme Ranked Set SampleAmerican Public Health Association (2011)
AbstractIn this paper we provide a more efficient nonparametric test of symmetry based on the empirical overlap coefficient using kernel density estimation applied to an extreme ranked set sample. Our investigation reveals that our proposed test of symmetry is more powerful than all available tests of symmetry. Intensive simulation is conducted to examine the power of the proposed test. An illustration is provided using cardiac output and body weight of neonates in a neonatal intensive care unit. Learning Areas: Biostatistics, economics; Protection of the public in relation to communicable diseases including prevention or control; Public health biology. Learning Objectives: To describe the need for researchers to check the underlying distribution assumptions before using any statistical procedures to analyze medical or public health data. Also, to compare different test procedure to decide which test statistics is more efficient for test for symmetry. Analyze data from Noninvasive Measurement of Cardiac Output by Electrical Velocimetry in Neonates data.
- Nonparametric test of symmetry,
- Tests of symmetry
Publication DateNovember 2, 2011
Citation InformationHani M. Samawi, Robert L. Vogel, Barbara Weaver and Joseph Van de Water. "An Optimal Nonparametric Test of Symmetry Based on the Overlapping Coefficient Using an Extreme Ranked Set Sample" American Public Health Association (2011)
Available at: http://works.bepress.com/robert_vogel/13/