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Full-Text Articles in Medicine and Health Sciences
A Test Of Symmetry Based On The Kernel Kullback-Leibler Information With Application To Base Deficit Data, Hani M. Samawi, Robert L. Vogel
A Test Of Symmetry Based On The Kernel Kullback-Leibler Information With Application To Base Deficit Data, Hani M. Samawi, Robert L. Vogel
Biostatistics Faculty Publications
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.
A More Efficient Nonparametric Test Of Symmetry Based On Overlapping Coefficient, Hani M. Samawi, Robert L. Vogel
A More Efficient Nonparametric Test Of Symmetry Based On Overlapping Coefficient, Hani M. Samawi, Robert L. Vogel
Biostatistics Faculty Publications
In 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 order statistics, namely extreme ranked set sampling. Our simulation investigation reveals that our proposed test of symmetry is at least as powerful as currently 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.
Inference On Overlapping Coefficients In Two Exponential Populations, Mohammad F. Al-Saleh, Hani M. Samawi
Inference On Overlapping Coefficients In Two Exponential Populations, Mohammad F. Al-Saleh, Hani M. Samawi
Biostatistics Faculty Publications
Three measures of overlap, namely Matusita’s measureρ , Morisita’s measure λ and Weitzman’s measure Δ are investigated in this article for two exponential populations with different means. It is well that the estimators of those measures of overlap are biased. The bias is of these estimators depends on the unknown overlap parameters. There are no closed-form, exact formulas, for those estimators variances or their exact sampling distributions. Monte Carlo evaluations are used to study the bias and precision of the proposed overlap measures. Bootstrap method and Taylor series approximation are used to construct confidence intervals for the overlap measures.