A More Efficient Nonparametric Test of Symmetry Based on Overlapping Coefficient

Hani M. Samawi; Robert L. Vogel

doi:10.15406/bbij.2014.01.00015

A More Efficient Nonparametric Test of Symmetry Based on Overlapping Coefficient

Hani M. Samawi, Robert L. Vogel

Georgia Southern University

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	American English
Journal	Biometrics and Biostatistics International Journal
Volume	1
DOIs	https://doi.org/10.15406/bbij.2014.01.00015
State	Published - Dec 16 2014

Keywords

Test of symmetry
Power of the test
Bootstrap method
Overlap coefficients
Weitzman's measure
Extreme ranked set sample
Kernel Density estimation

DC Disciplines

Biostatistics
Community Health
Public Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.15406/bbij.2014.01.00015License: CC BY

A More Efficient Nonparametric Test of Symmetry Based on OverlappFinal published version, 658 KBLicense: CC BY
A More Efficient Nonparametric Test of Symmetry Based on OverlappFinal published version, 658 KBLicense: CC BY

Cite this

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abstract = " 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.",

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AU - Vogel, Robert L.

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N2 - 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.

AB - 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.

KW - Test of symmetry

KW - Power of the test

KW - Bootstrap method

KW - Overlap coefficients

KW - Weitzman's measure

KW - Extreme ranked set sample

KW - Kernel Density estimation

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpubs/91

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DO - 10.15406/bbij.2014.01.00015

M3 - Article

VL - 1

JO - Biometrics and Biostatistics International Journal

JF - Biometrics and Biostatistics International Journal

ER -

A More Efficient Nonparametric Test of Symmetry Based on Overlapping Coefficient

Abstract

Keywords

DC Disciplines

UN SDGs

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